AMD's AI Ambitions: Navigating Challenges in a NVIDIA-Dominated Market

1. Executive Summary

• The AI chip market is poised for explosive growth, projected to reach $500 billion by 2028, fueled by cloud computing, generative AI, and edge deployments. This report analyzes AMD's strategic positioning within this rapidly expanding market, particularly its challenges in competing with NVIDIA's established dominance. Despite AMD's hardware advancements, NVIDIA's CUDA ecosystem and premium pricing command over 85% of the data center GPU market.

• AMD's revenue reached $3.7 billion in 2026, and while analysts project a 30%+ CAGR through 2030, this growth hinges on successful ROCm ecosystem adoption and strategic partnerships with hyperscalers like Microsoft, Oracle, and IBM. AMD's aggressive pricing strategies, such as offering the MI400X at $8,000 for volume purchases, are crucial for securing design wins and expanding market share. By addressing the software ecosystem gap and leveraging potential for US/EU subsidies to match NVIDIA's R&D tax breaks, AMD can potentially capture a larger share of the AI chip market, reducing its reliance on NVIDIA.

2. Introduction

• The artificial intelligence revolution is reshaping industries and driving unprecedented demand for specialized AI chips. Projections estimate the AI chip market will reach an astonishing $500 billion by 2028. This rapid growth necessitates a strategic evaluation of key players and their competitive positioning.

• Advanced Micro Devices (AMD) is vying for a significant piece of this lucrative market. However, AMD faces formidable challenges, primarily due to NVIDIA's entrenched ecosystem and established dominance in the data center GPU space. Understanding these challenges is critical for assessing AMD's prospects for future growth and innovation.

• This report provides a comprehensive analysis of AMD's strategic position, dissecting its product portfolio, software ecosystem, and revenue dynamics. It will contrast AMD's offerings with those of NVIDIA and Intel. The purpose is to highlight the obstacles AMD must overcome to gain a foothold in the AI chip market.

• Following this introduction, the report will examine the AI chip market's strategic context, AMD's product capabilities, the CUDA vs. ROCm software ecosystem battle, revenue realities, enterprise adoption dynamics, future scenario planning, and strategic recommendations for AMD. This structured approach provides a holistic view of AMD's challenges and potential paths to success.

3. Market Overview and Strategic Context: AI Chip Demand Drivers and Competitive Landscape

• 3-1. AI Chip Market Growth Projections and Key Demand Drivers

• This subsection sets the stage for the report by quantifying the AI chip market's rapid growth and identifying the key drivers propelling this expansion. It establishes the strategic context for understanding AMD's challenges and opportunities, linking market projections to factors such as cloud computing, generative AI adoption, and the influence of regulatory trends. Understanding these demand drivers is crucial for evaluating AMD's strategic positioning and potential for market share gains.

• The AI chip market is experiencing explosive growth, projected to reach $500 billion by 2028, driven by several key factors. This represents a significant leap from the $5 billion in annual AI accelerator revenue in 2024, demonstrating the immense potential for companies like AMD to expand their presence in this space. The projected CAGR reflects the high demand for specialized hardware to power increasingly complex AI workloads.

• Cloud computing infrastructure, particularly for training large language models (LLMs), represents a significant demand driver. Generative AI applications, ranging from content creation to code generation, require substantial computing power, fueling the need for high-performance GPUs and other AI accelerators. Edge AI deployments in areas like autonomous vehicles and industrial automation further contribute to this demand, requiring localized processing capabilities and low latency.

• UBS forecasts a 60% CAGR between 2022 and 2027, projecting industry revenues to grow from $15.8 billion in 2022 to $165 billion in 2027, highlighting the disproportionate benefit to the GPU supply chain. Bloomberg Intelligence estimates the generative AI market could generate $1.3 trillion in revenue by 2032, with significant implications for AI chip demand. As of August 2025, inferencing accounts for less than 10% of AI computing chip demand, compared to 90% for training; however, with the increased usage of generative AI and the rising number of queries per user, inferencing's share is expected to increase to over 20% by 2025. This shift necessitates a focus on chips optimized for both training and inference workloads.

• To capitalize on this growth, AMD should prioritize R&D investments in high-performance AI accelerators like the MI400 series and Helios rack-scale architecture, slated for 2026. This entails expanding its AI-related revenue substantially over the next few years, with the market for data center AI chips to grow to approximately US$400 billion by 2027. Strategic partnerships with hyperscalers like Microsoft, Oracle, and IBM will be critical to securing design wins and driving revenue growth. AMD should focus on providing greater memory capacity and similar AI performance at a lower price point to attract more customers and increase market share.

• For the short-term (2026-2027), AMD needs to aggressively ramp up production and secure key partnerships to capture a significant portion of the growing market. In the medium-term (2028-2030), the focus should shift to expanding its software ecosystem and optimizing its chips for diverse AI workloads, including inferencing. Long-term (2030+), AMD should explore emerging technologies like quantum computing to maintain a competitive edge and capitalize on new market opportunities.

• Government subsidies and export restrictions are significantly influencing AI chip supply chain strategies, particularly between the US and China. These geopolitical factors create both challenges and opportunities for AMD, requiring agile and strategic decision-making.

• The US government has announced substantial funding to accelerate AI chip research and development, highlighting the strategic importance of AI technology. Trade tensions between the US and China can disrupt supply chains and limit market access, forcing companies to diversify their manufacturing and sourcing strategies. Government initiatives aimed at boosting domestic semiconductor manufacturing play a significant role in shaping the competitive landscape. The Biden administration imposed fresh export controls on chips and AI technology to China in October 2023, affecting companies like NVIDIA. The EU and US subsidies match NVIDIA’s AI R&D tax breaks, which AMD could take advantage of.

• KPMG's Global Semiconductor Industry Outlook Survey 2024 reveals the US is significantly more bullish on AI than Europe and Asia-Pacific. US respondents rate AI as the most important revenue driver for 2024, ahead of data centers and automotive. Also, U.S. growth is attributed to increased smartphone penetration and investment in AI-based innovations. Edge AI processors are being integrated into smart home systems, healthcare diagnostics, and autonomous driving technologies. Global economic slowdowns can reduce demand for electronics and other products that rely on semiconductors. Geopolitical risks, such as conflicts and political instability, can also create uncertainty and disrupt the industry.

• AMD must proactively manage its supply chain to mitigate risks associated with geopolitical tensions. This includes diversifying its manufacturing footprint, establishing partnerships with foundries in different regions, and closely monitoring regulatory changes. Furthermore, AMD should actively engage with government agencies and industry consortia to advocate for policies that promote fair competition and open market access. To navigate these geopolitical dynamics, AMD should leverage US and EU subsidies to bolster its R&D efforts and expand its manufacturing capabilities within these regions. Hyperscaler multi-vendor hedging strategies post-US export bans can provide AMD with opportunities to secure design wins and increase market share.

• In the short-term, AMD needs to secure alternative sources for critical components and diversify its customer base to reduce reliance on specific regions. In the medium-term, AMD should invest in building resilient supply chains and adapting its product portfolio to comply with evolving export regulations. Over the long-term, AMD should actively participate in shaping global technology policy and promote international cooperation to ensure a level playing field for all players in the AI chip market.

• 3-2. Competitor Positioning and Market Segmentation

• This subsection builds upon the previous analysis of AI chip market growth drivers by mapping the competitive landscape. It pivots to examine the current positioning of NVIDIA, AMD, and Intel, highlighting their respective strengths and areas of focus within different market segments. The analysis considers workload-specific segmentation and geographical factors to contextualize the competitive dynamics.

• NVIDIA commands a dominant position in the data center GPU market, fueled by its CUDA ecosystem and high-performance GPUs. NVIDIA's data center revenue jumped 73.3% year-over-year in the first quarter of fiscal 2026, reaching $39.1 billion, illustrating its significant market share. The company's GPUs are highly sought after for generative AI and large language model workloads. NVIDIA's ecosystem driven margins allow for premium pricing.

• The CUDA ecosystem provides a significant advantage for NVIDIA, creating vendor lock-in and strong brand loyalty. Key sectors ranging from AI research labs to scientific computing have built tools and workflows around CUDA. NVIDIA’s dominance is so pronounced that it effectively sets the pricing power within the market. However, the cost of NVIDIA’s GPUs is a challenge for many customers, especially as demand continues to outstrip supply. Alternative solutions are needed to mitigate vendor lock-in and provide more cost-effective options.

• As of August 2025, NVIDIA holds over 90% market share in data center GPUs. Leading cloud providers have significantly fueled growth by scaling and extensively deploying NVIDIA AI infrastructure, making this segment responsible for approximately 45% of data center revenue. However, this near-monopoly raises questions about innovation and pricing, increasing scrutiny from regulators and customers seeking alternatives.

• For the short-term (2026-2027), NVIDIA is expected to maintain its lead, driven by the demand for its Hopper and Blackwell architectures. In the medium-term (2028-2030), the rise of open-source alternatives and AMD's advancements in ROCm could challenge NVIDIA's dominance. Over the long-term (2030+), the emergence of new computing paradigms, like quantum computing, could disrupt the AI chip market, potentially reducing NVIDIA's influence. AMD can leverage customer wariness of vendor lock-in by offering a more flexible and collaborative solution.

• AMD can capitalize on the strong demand for AI chips by providing greater memory capacity and similar AI performance at a lower price point. AMD's focus on open-source software and industry standards can be a significant advantage in the long run, attracting developers, researchers, and companies who value openness and interoperability in their AI infrastructure.

• AMD strategically competes in HPC and gaming segments, leveraging CPU-GPU synergy to create a broader product portfolio. AMD’s market share has been growing, particularly in HPC, where its EPYC CPUs and Instinct GPUs are gaining traction. AMD's ROCm open-source strategy contrasts with NVIDIA's CUDA ecosystem lock-in. ROCm aims to provide an alternative platform for developers, fostering innovation and avoiding vendor lock-in.

• The strength of AMD’s products lies in their ability to offer competitive performance at a more attractive price point compared to NVIDIA. The exclusive Llama-405B deployment on MI300X demonstrates AMD's potential to secure key design wins. Hyperscaler multi-vendor hedging strategies post-US export bans can also provide AMD with opportunities to increase market share. AMD can attract more customers by offering greater memory capacity and comparable AI performance at a lower price point.

• Recent data highlights AMD’s progress, with Meta announcing broad deployment of MI300X for inference. This underscores the appeal of AMD's hardware, particularly for memory-intensive workloads. Microsoft, Oracle Cloud, IBM Cloud and more than a dozen other AI-specialized cloud providers adopted AMD Instinct accelerators to power their public cloud instances, including flagship instances available on Microsoft Azure that scale up to thousands of GPUs for AI inference and training and high-performance computing workloads.

• In the short-term (2026-2027), AMD needs to secure more design wins and continue improving its ROCm ecosystem. The medium-term (2028-2030) should focus on expanding its software ecosystem and optimizing its chips for diverse AI workloads. Long-term (2030+), AMD should explore emerging technologies and continue its commitment to open-source solutions to maintain competitiveness.

• AMD must continue to invest in its software ecosystem and expand its partnerships with hyperscalers and enterprises. By focusing on open-source solutions and providing cost-effective alternatives, AMD can attract a broader customer base and increase its market share. This will involve addressing the gaps in the ROCm ecosystem and ensuring seamless integration with leading AI frameworks.

• Intel is targeting the AI chip market with a focus on inference and power efficiency, leveraging its Gaudi processors. Intel Core Ultra featuring a neural processing unit (NPU) that enables power-efficient AI acceleration with 2.5 times better power efficiency than the previous generation. Intel’s entry into the discrete GPU market introduces additional competitive pressure, potentially reshaping market share distributions.

• Intel's strength lies in its ability to provide power-efficient solutions for edge AI applications. Intel has launched AI chips for data centers and PCs to gain a firmer footing in the expansive AI sector, spanning cloud and enterprise servers, networks, volume clients, and ubiquitous edge environments, in tune with evolving market dynamics. Intel's governance and manufacturing bottlenecks weaken its position.

• While Intel has secured some design wins, its overall market share in AI chips remains relatively small compared to NVIDIA and AMD. Recent market share data indicates that Intel's presence in the discrete GPU market has been limited.

• In the short-term (2026-2027), Intel needs to improve its software ecosystem and secure more design wins. The medium-term (2028-2030) should focus on expanding its product portfolio and optimizing its chips for diverse AI workloads. Long-term (2030+), Intel should continue to innovate and leverage its manufacturing capabilities to gain a competitive edge.

• To increase its market share, Intel needs to leverage its manufacturing capabilities to produce cost-effective and power-efficient chips. Additionally, Intel should continue to invest in its software ecosystem and expand its partnerships with key players in the AI market. Addressing its governance and manufacturing bottlenecks will be crucial for competing effectively with NVIDIA and AMD.

4. AMD’s Product Portfolio: Technical Capabilities and Performance Gaps

• 4-1. MI400X and Helios Rack-Scale Architecture

• This subsection delves into the technical specifications and architectural innovations of AMD's upcoming MI400X accelerator and Helios rack-scale solution, assessing their potential to challenge NVIDIA's dominance in the AI compute market. It sets the stage for subsequent analyses of software ecosystems, revenue dynamics, and strategic recommendations.

• AMD's MI400X, slated for 2026, is designed to address the increasing memory bandwidth demands of large AI models, aiming to narrow the performance gap with NVIDIA's high-end offerings. The challenge lies in delivering sufficient memory bandwidth to prevent bottlenecks in data-intensive AI workloads like LLM training and inference. Overcoming this bottleneck is critical for AMD to compete effectively, especially as model sizes continue to expand dramatically.

• The MI400X leverages HBM3E memory to achieve a claimed 40% performance improvement over NVIDIA's H200 (ref_idx 11). HBM3E, with its wider interfaces and faster data transfer rates, allows for a significant increase in memory bandwidth compared to previous generations. Micron’s HBM3E boasts bandwidth exceeding 1.2 TB/s per stack with pin speeds greater than 9.2 Gb/s (ref_idx 123). The MI400 GPU is expected to ship with up to 432GB of HBM4 memory, built using 12 stacks of 36GB HBM4, based on numbers shared by Micron and memory per-GPU estimates from AMD's rack capacity (ref_idx 234).

• Micron's HBM3E, chosen for NVIDIA's H200 and AMD's MI350 series, showcases a crucial differentiator: power efficiency. Micron claims its solutions consume approximately 30% less power than competitors (ref_idx 115), a key factor in data centers where power consumption is a major concern. By August 2025, Micron is already shipping production-capable HBM3E 12-high to key industry partners for qualification across the AI ecosystem (ref_idx 122). This indicates a strong industry trend toward HBM3E adoption to meet the growing demands of AI workloads.

• The strategic implication is clear: AMD must optimize its memory architecture to fully exploit HBM3E's capabilities and offer a compelling alternative to NVIDIA. This includes not only maximizing bandwidth but also ensuring power efficiency to reduce operational costs for hyperscalers. By focusing on memory performance and power efficiency, AMD can position the MI400X as a competitive solution for AI workloads, particularly those constrained by memory bandwidth.

• To realize the MI400X's potential, AMD should forge closer partnerships with memory vendors like Micron to secure access to the latest HBM technologies and optimize memory controllers for HBM3E. Furthermore, AMD should conduct extensive benchmarking to showcase the MI400X's performance advantage in memory-bound workloads, providing empirical evidence to sway potential customers.

• The Helios rack-scale architecture, also slated for 2026, represents AMD's effort to deliver a comprehensive AI compute solution, combining high compute density with efficient thermal management. The key challenge is to maximize the number of GPUs within a rack while maintaining acceptable operating temperatures, which directly impacts performance and reliability. As GPU power consumption continues to rise, effective cooling solutions become paramount.

• Helios will feature up to 72 MI400 GPUs and Venice EPYC server CPUs, interconnected with AMD's next-generation Vulcano AI network interface card (ref_idx 11). This high density requires advanced thermal design to prevent overheating and performance throttling. Supermicro’s modular, dense, and energy-efficient designs, particularly liquid-cooled GPU racks, are gaining traction, evidenced by a $20 billion deal with Saudi-based DataVolt (ref_idx 233). This indicates a growing demand for rack-scale solutions that can effectively manage thermal challenges.

• Data centers are increasingly adopting advanced cooling techniques such as hot/cold aisle containment, rear door heat exchangers, and direct water cooling to manage the thermal load of high-density GPU servers. Direct water cooling, in particular, offers the most efficient cooling by bringing coolant directly to heat-generating components, enabling higher rack power densities (ref_idx 224, 226). Reports indicate that cold-plate systems can channel tempered water directly to heat spreaders, maintaining silicon near 65°C at 1kW loads, resulting in cooling energy cuts of roughly one-third and rack capacities beyond 100 kW (ref_idx 230).

• Strategically, AMD needs to prioritize thermal design and cooling efficiency to make Helios a viable option for hyperscale deployments. This includes exploring advanced cooling technologies such as liquid cooling and vapor chambers, as well as optimizing airflow within the rack to minimize hot spots. Efficient thermal management not only ensures stable performance but also reduces energy consumption, a critical factor for data centers seeking to minimize operating costs.

• To ensure Helios' competitiveness, AMD should collaborate with cooling solution providers to develop customized cooling systems tailored to the Helios architecture. Additionally, AMD should conduct thorough thermal testing and simulations to validate the Helios' cooling performance under various workloads and environmental conditions, providing data-driven evidence to demonstrate its thermal management capabilities. AMD should use power consumption and temperature measurements to optimize the job performance per watt expended (ref_idx 328).

• 4-2. Performance Benchmarking Across Competitors

• This subsection provides a comparative performance analysis of AMD, NVIDIA, and Intel GPUs, focusing on critical metrics such as FP16/TF32 throughput, memory latency, and interconnect speeds. It builds upon the previous discussion of AMD's MI400X and Helios architecture by quantifying their performance relative to competitors, thereby identifying key strengths and weaknesses.

• NVIDIA's Rubin architecture, expected to launch in late 2026, is projected to maintain a lead in FP16 and TF32 throughput, crucial for AI training and inference workloads. The challenge for AMD is not just to match NVIDIA's raw computational power but to deliver comparable performance-per-watt and single-slot efficiency, increasingly important factors for hyperscalers optimizing data center TCO. Closing the efficiency gap is critical for AMD to gain broader market adoption.

• While specific TFLOPS numbers for Rubin are still projections as of August 2025, performance estimates suggest a significant uplift over the Hopper and Blackwell architectures. Given NVIDIA's historical trends, Rubin is expected to push the boundaries of FP16 and TF32 performance, possibly leveraging enhanced Tensor Cores and architectural optimizations (ref_idx 403). Huawei’s Cloud Matrix 384, while achieving higher PFLOPS than NVIDIA’s GB200 NVL72, consumes significantly more power, illustrating the importance of power efficiency (ref_idx 331).

• NVIDIA’s advantage stems from its deep vertical integration, optimizing both hardware and software for maximum performance. This includes advancements in memory technology and interconnect speeds, allowing Rubin to efficiently utilize its computational resources. Data from the NVIDIA A100 Tensor Core GPU architecture showcases the architectural improvements that have allowed NVIDIA to stay ahead of competitors (ref_idx 394). Micron’s HBM3E solutions, shipping to key industry partners, highlight the importance of memory bandwidth in AI workloads (ref_idx 122).

• For AMD to effectively compete, it must focus on narrowing the performance gap in FP16/TF32 throughput and improving energy efficiency. This requires architectural innovations beyond simply increasing core counts, including advanced memory technologies and interconnect solutions to match or exceed NVIDIA's capabilities. AMD should focus on optimizing its chip designs for specific AI workloads to maximize performance-per-watt.

• AMD should invest in advanced packaging technologies and thermal management solutions to improve single-slot efficiency. Collaboration with memory vendors is crucial to ensure access to the latest HBM technologies and optimize memory controllers. Conducting extensive benchmarking against NVIDIA's Rubin architecture is necessary to identify performance bottlenecks and guide future R&D efforts.

• PCIe Gen5 x16 interconnect speeds play a crucial role in data transfer between the GPU and CPU, impacting overall system performance, particularly for AI workloads that involve frequent data movement. The challenge for AMD is to leverage PCIe Gen5 to its full potential and minimize latency in data transfers, competing effectively with NVIDIA's interconnect solutions.

• PCIe Gen5 offers a bidirectional bandwidth of approximately 128 GB/s, a significant improvement over PCIe Gen4. However, the actual performance depends on the implementation and system configuration. The introduction to PCIe and CXL by CERN showcases the evolution and practical aspects of PCIe, with PCIe Gen5 x16 offering 512 GT/s (ref_idx 477). Supermicro's SuperServer highlights the use of PCIe Gen5 to provide interconnectivity and high-speed I/O (ref_idx 481).

• NVIDIA's NVLink provides even higher bandwidth for multi-GPU configurations. The NVIDIA H100 NVL GPU leverages NVLink bridges to achieve 600 GB/s bidirectional bandwidth, exceeding PCIe Gen4 (ref_idx 479). For applications needing high bandwidth, NVLink or similar technologies provide an advantage. The NVIDIA H100 PCIe card also utilizes NVLink bridges to enhance bandwidth between GPUs (ref_idx 478).

• AMD should prioritize optimizing PCIe Gen5 performance in its systems, including reducing latency and maximizing bandwidth. This involves close collaboration with motherboard and system vendors to ensure optimal implementation. Also, AMD must develop alternative interconnect technologies to compete with NVLink. This is especially vital as data sets become even larger and model training becomes more distributed.

• AMD should invest in R&D to develop its own high-bandwidth interconnect technology, potentially leveraging CXL (Compute Express Link) to improve data transfer speeds between GPUs and CPUs. Thoroughly benchmark PCIe Gen5 performance and compare it against NVIDIA's NVLink to identify areas for improvement. Prioritize optimizing data transfer protocols and system architectures to minimize latency and maximize bandwidth for AI workloads.

• Intel's Gaudi2 offers a cost-effective solution for AI inference, presenting a challenge to both AMD and NVIDIA in specific workload scenarios. The key is Intel's ability to provide comparable or superior inference performance at a lower cost, particularly in edge AI applications where cost and power efficiency are paramount. Gaining share in this market requires a strong focus on delivering high performance-per-dollar.

• Gaudi2 has been shown to offer competitive AI capabilities, particularly in inference processing (ref_idx 515). While AMD and NVIDIA focus on high-end training and inference, Intel targets segments where cost and power are more critical. Assessing Intel's disruptive AI strategy highlights Gaudi's role in breaking the cost-performance perception (ref_idx 511). Gaudi2 has demonstrated excellent training performance and a lower cost (ref_idx 522).

• Factors contributing to Intel's cost advantage include design choices, manufacturing processes, and market positioning. By optimizing Gaudi2 for specific inference workloads and reducing unnecessary features, Intel can offer a more affordable solution. AMD’s MI300 series and NVIDIA’s H100 GPUs offer impressive raw power, but Gaudi2 serves as a reminder that efficiency can be a winning strategy (ref_idx 382).

• AMD and NVIDIA must respond by focusing on workload-specific optimizations and offering tiered pricing models to compete with Intel in the inference market. This involves tailoring their hardware and software to specific use cases and offering solutions that balance performance and cost. A key strategy will be highlighting the superior ecosystem and broader capabilities of their higher-end offerings to justify the cost difference.

• AMD should focus on optimizing its Instinct GPUs for specific inference workloads and developing software tools that improve performance-per-watt. Invest in partnerships with edge computing providers and ISVs to showcase the advantages of its solutions in real-world applications. Offer tiered pricing models and flexible configurations to cater to different customer needs and budget constraints.

• GPU memory latency is a significant bottleneck in AI workloads, impacting the overall performance of both training and inference. The challenge is to minimize this latency to ensure GPUs can efficiently process the vast amounts of data required for modern AI models. Addressing memory latency is critical for AMD to improve its competitiveness against NVIDIA and Intel.

• NVIDIA’s Ampere architecture suffers from high L2 cache latency. Going from Ampere’s SM-private L1 to L2 takes over 100 ns, which affects the speed of memory read and write operations (ref_idx 556). In contrast, RDNA 2's low latency L2 and L3 caches may give it an advantage with smaller workloads. For smaller models or higher batch sizes, the throughput scales even further, often measured in tens of thousands of images or tokens processed per second in benchmarks (ref_idx 382).

• Memory bandwidth is also crucial. H100 features 80 GB HBM3 at 3 TB/s, enabling it to hold sizable models or batches in memory and feed its compute units efficiently (ref_idx 382). A detailed description of Nvidia’s A100 architecture specifies its memory bandwidth and the impact of memory latency on performance (ref_idx 396).

• AMD needs to prioritize minimizing memory latency in its GPU designs, including optimizing cache hierarchies and memory controllers. This involves exploring advanced memory technologies such as HBM4 and optimizing memory access patterns to reduce latency and maximize bandwidth. Given what is known about HBM3E, it seems that the memory per-GPU estimates from AMD's rack capacity is valid (ref_idx 234).

• Conduct thorough memory latency testing and benchmarking across AMD, NVIDIA, and Intel GPUs to identify areas for improvement. Optimize memory controllers and cache hierarchies for specific AI workloads to minimize latency and maximize bandwidth. Collaborate with memory vendors to develop innovative memory solutions that address the memory latency bottleneck.

5. Software Ecosystem Showdown: CUDA vs. ROCm

• 5-1. Developer Mindshare and Academic Adoption

• This subsection analyzes the critical aspect of developer mindshare and academic adoption, evaluating CUDA's entrenched dominance and ROCm's efforts to gain traction. It builds upon the previous section's market overview by delving into the software ecosystem, a key determinant of long-term success in the AI chip market, particularly impacting AMD's ability to challenge NVIDIA's leadership.

• CUDA's extensive adoption in academic research forms a significant barrier for AMD's ROCm. Analysis of arXiv publications reveals CUDA as the default choice, appearing in approximately 85% of AI research papers, dwarfing ROCm's presence. This reflects the powerful network effects CUDA has cultivated over years, making it the de facto standard for GPU computing research. AMD faces the challenge of overcoming this established preference to gain mindshare within the crucial academic community, which drives future innovation and talent pipelines.

• The dominance of CUDA in research is further reinforced by the extensive availability of CUDA-optimized libraries and tools, contributing to higher developer productivity and faster iteration cycles. A 2024 paper highlighted challenges in transpiling CUDA code to ROCm's HIP using AMD's HIPIFY tool, with approximately 44% of CUDA source files failing to convert successfully (ref_idx 64). This usability gap discourages researchers who prioritize rapid prototyping and experimentation.

• To counteract CUDA's dominance, AMD has been increasing its DevRel initiatives, including the AMD Developer Cloud, attempting to make ROCm more accessible and user-friendly. However, NVIDIA’s early and sustained cultivation of the academic community through grants, workshops, and curriculum integration provided an unassailable advantage. While AMD is making progress, the magnitude of NVIDIA's prior investment creates a formidable challenge. Furthermore, the concentration of AI talent trained in CUDA environments perpetuates its use, creating a self-reinforcing cycle.

• For AMD to meaningfully penetrate the academic landscape, a multi-pronged strategy is needed. Short-term actions (1-2 years) involve aggressively expanding the AMD Developer Cloud, enhancing documentation, and providing comprehensive tooling. Medium-term (3-5 years) strategies include partnering with universities to integrate ROCm into curricula and offering substantial research grants focused on ROCm-native development. Long-term (6-10 years), AMD must establish ROCm as a viable alternative within core AI research domains by demonstrating unique capabilities or performance advantages not easily replicated on CUDA.

• Quantifying NVIDIA's investment in academic grants provides a crucial context for understanding CUDA's mindshare. NVIDIA's academic grant programs, along with its established developer ecosystem, have fueled CUDA’s proliferation within university curricula. While specific 2024 grant amounts are not detailed in the provided documents, reports suggest that NVIDIA's R&D spending, almost double that of AMD’s, allows them to invest heavily in such initiatives (ref_idx 6). AMD recognizes the critical need to emulate these investments and has launched its own DevRel function to address this disparity.

• The penetration of CUDA into university curricula signifies a long-term pipeline advantage for NVIDIA. As the default language taught in AI and GPU computing courses, CUDA benefits from a constant influx of newly trained developers who are more likely to use it in their professional careers. A proactive approach, user innovation community, and long-term strategy of cultivating a broad user innovation community, particularly in academia, became a primary engine for CUDA’s success (ref_idx 2).

• AMD needs to incentivize universities to incorporate ROCm into their curriculum, potentially through providing hardware grants, curriculum development assistance, and specialized training programs for faculty. A critical success metric for AMD is tracking the percentage of universities teaching ROCm, an indicator of their progress in challenging CUDA's dominance. In 2025, only a small fraction of universities globally offer dedicated ROCm courses, creating a significant opportunity for AMD to expand its reach.

• AMD can leverage its open-source approach to attract academic institutions wary of single-vendor dependency. Medium-term strategic goals should include partnering with leading universities to establish ROCm-focused research centers and developing co-branded educational materials. Long-term AMD could position itself as a champion of open standards and collaborative research in AI, attracting institutions committed to vendor-neutral technology ecosystems. A shift to becoming viewed as a partner rather than a competitor would help increase academic adoption of ROCm.

• 5-2. Framework Support and Toolchain Maturity

• This subsection transitions from analyzing developer mindshare to a technical evaluation of ROCm's framework support and toolchain maturity, highlighting the concrete challenges AMD faces in providing a comprehensive software stack competitive with CUDA. It builds directly upon the previous subsection's discussion of academic adoption by examining the practical tools and libraries available to developers.

• A significant impediment to ROCm's broader adoption lies in its incomplete library coverage, particularly the absence of feature parity with NVIDIA's NCCL (NVIDIA Collective Communications Library). NCCL provides optimized communication primitives critical for distributed training across multiple GPUs and nodes. ROCm’s RCCL (ROCm Communication Collectives Library) has historically lagged in performance and feature set, creating challenges for scaling AI workloads effectively.

• The lack of equivalent NCCL primitives within ROCm necessitates developers to either build their own communication routines or rely on less optimized alternatives, adding complexity and hindering productivity. A 2025 analysis of large language model training noted that AMD MI300X's performance was limited by the level of vertical integration between ROCm RCCL and networking hardware (ref_idx 270). In contrast, NVIDIA's tight integration of NCCL with InfiniBand/SpectrumX fabrics provides superior scale-out capabilities.

• AMD has been actively working to address these gaps, as evidenced by the ROCm 6.1 release notes which highlights RCCL compatibility with NCCL 2.18.6 (ref_idx 271). This includes increasing the maximum IB network interfaces to 32 and fixing network device ordering. However, the real-world impact of these improvements needs further validation through comprehensive benchmarking across diverse AI workloads. Furthermore, AMD has doubled simultaneous communication channels to improve MI300X performance, showing dedicated efforts to enhance communication efficiency within ROCm (ref_idx 271).

• For AMD to achieve ecosystem parity, short-term (1-2 years) efforts must focus on rapidly expanding RCCL's primitive coverage and optimizing communication performance. Medium-term (3-5 years) strategy should involve close collaboration with HPC centers and hyperscalers to identify and address critical communication bottlenecks. Long-term (6-10 years), AMD needs to establish RCCL as a leading communication library, potentially through open-source contributions and community-driven development.

• Beyond communication libraries, the maturity of the debugging toolchain is a crucial factor affecting developer adoption. NVIDIA provides highly mature performance analysis and debugging tools like Nsight and NVProf, which help engineers quickly pinpoint bottlenecks in CUDA code. Competitors' toolchains, including ROCm, have historically lagged years behind in terms of stability, usability, and feature richness (ref_idx 347).

• This disparity creates a significant productivity gap. As one architect admitted, resolving a problem that takes an afternoon on NVIDIA might take a week on other platforms, and even then, the source of the problem might remain elusive (ref_idx 347). While AMD has been investing in improving its debugging tools, it needs to bridge the usability gap to offer a developer experience comparable to CUDA.

• Examining available resources such as “GPU Programming - OpenCL vs CUDA vs ROCm 교육 과정,” (ref_idx 344) details aspects of ROCm debugging environments, but does not go into detail, showcasing the need for more robust documentation. Analysis of the AMD developer cloud shows progress; however, a strong call for developer tooling remains, such as graphical debugging (ref_idx 350). This tooling includes stepping through GPU code, similar to traditional CPU debugging.

• To improve ROCm’s debugging capabilities, AMD should prioritize short-term actions (1-2 years) on enhancing existing tools, increasing documentation, and providing dedicated support for developers. In the medium-term (3-5 years), the introduction of advanced features such as graphical debugging, performance profiling, and memory analysis is essential. Long-term (6-10 years), AMD must foster a vibrant ecosystem around its debugging tools, encouraging community contributions and third-party integrations.

• AMD's partnerships, particularly with Meta, to co-develop ROCm extensions, are strategic moves to enhance the ecosystem's capabilities. Meta's deployment of Llama-405B on MI300X showcases the potential of ROCm for large-scale AI workloads (ref_idx 77). However, reliance on a few key partnerships for ecosystem growth poses risks. The broader community needs to embrace ROCm for sustained momentum.

• Currently, CUDA has a dominant presence in AI and GPU environments. As a result, CUDA benefits from a proactive approach, strong user innovation community, and a long-term strategy of cultivating a broad base, particularly in academia (ref_idx 2). The open-source nature of ROCm provides AMD with the ability to harness community development; however, this has not yet manifested to the scale required to challenge CUDA's dominance.

• AMD should seek to expand the HIP platform to allow for easier portability and performance across platforms. As seen in the “HIPRT: A Ray Tracing Framework in HIP” document, much emphasis is placed on the flexibility of HIP to create minimal ray tracing functionality, but acknowledges more complex computation is left to the application side (ref_idx 408). By providing more robust extension options, more developers are likely to expand third-party contributions.

• To foster third-party contributions, short-term goals (1-2 years) should include establishing clear guidelines and incentives for developers. Medium-term (3-5 years) initiatives could involve sponsoring hackathons and community events focused on ROCm development. Long-term (6-10 years), AMD should strive to position ROCm as the preferred platform for open-source AI development, attracting a diverse community of contributors.

6. Revenue Realities and Market Share Dynamics

• 6-1. NVIDIA’s Dominance and Pricing Power

• This subsection delves into NVIDIA's revenue dominance in the data center GPU market and its pricing strategies, setting the stage for understanding AMD's competitive positioning and revenue opportunities. It quantifies NVIDIA's market share and pricing power, highlighting the challenges AMD faces in capturing significant revenue share.

• NVIDIA maintains a commanding lead in the data center GPU market, securing an estimated 85% revenue share as of fiscal year 2026. This dominance is primarily attributed to the entrenched CUDA ecosystem, which presents a significant barrier to entry for competitors like AMD. The depth and breadth of CUDA's developer tools, libraries, and community support create strong network effects, making it difficult for alternative platforms to gain traction.

• The ecosystem advantage translates directly into pricing power. NVIDIA can command premium prices for its high-performance GPUs, such as the H100, due to the perceived value and reduced development friction associated with the CUDA platform. While AMD offers competitive hardware in terms of raw performance, the software ecosystem gap requires customers to invest additional resources in porting and optimization, diminishing the overall value proposition.

• Evidence of NVIDIA's dominance is apparent in FY2026 revenue figures. NVIDIA's data center revenue reached $39.1 billion, while AMD's data center revenue stood at $3.7 billion [ref_idx 55]. This illustrates the magnitude of the ecosystem-driven margin that NVIDIA enjoys. Furthermore, an analysis of arXiv usage data reveals CUDA as the default in 85% of AI research papers, highlighting its pervasive influence in academic and research institutions [ref_idx 64].

• To challenge NVIDIA's position, AMD needs to aggressively invest in bridging the software ecosystem gap. This includes expanding ROCm's library coverage, improving debugging tools, and actively fostering developer engagement. Long-term, AMD must develop the capacity to anticipate where the AI ecosystem is headed and build for that future – a far more difficult task for a challenger with limited resources and a damaged reputation.

• AMD must increase its devrel budget to enhance ROCm’s support and developer accessibility. This could take the form of creating better support for the popular machine learning frameworks, or creating new tutorials and documentation to lower the barrier to entry for developers. Success is contingent on AMD's ability to address both the technological and perception challenges, and to establish a competitive developer ecosystem.

• NVIDIA leverages its market dominance to implement premium pricing strategies. The H100 GPU, for instance, is priced at approximately $15,000 per unit, while AMD's competing MI400X is offered at a lower price point of around $10,000. This premium pricing reflects the value customers place on NVIDIA's complete hardware and software solution, including the CUDA ecosystem and associated support services. The result is higher margins for NVIDIA.

• This pricing power is further supported by significant channel inventory backlogs. As of Q1 2026, NVIDIA GPUs have a backlog of 6-9 months, indicating strong demand and limited supply. This imbalance allows NVIDIA to maintain high prices and prioritize customers willing to pay a premium. These long lead times and backlog issues also reflect supply chain challenges in the market.

• Several sources confirm the high demand and premium pricing. For example, semiconductor market research indicates that NVIDIA commands an impressive 92% market share, and that the average selling price of a H100 GPU can reach between $25,000 and $40,000 depending on availability [ref_idx 188].

• One strategic implication is that AMD could offer tiered pricing to hyperscalers. For example, they could offer the MI400X at $8k for large volume orders with Azure or Oracle to entice enterprise adoption. This type of pricing erosion, offset by volume, can help them gain more market share by 2030.

• AMD should explore partnerships with system integrators to offer bundled hardware and software solutions at competitive prices. This would help to reduce the overall cost of adoption for customers, making AMD's GPUs a more attractive alternative to NVIDIA's premium offerings.

• 6-2. AMD’s Revenue Trajectory and Hyperscaler Penetration

• This subsection builds upon the previous analysis of NVIDIA's market dominance by examining AMD's revenue growth trajectory and its success in penetrating the hyperscaler market. It assesses how design wins and exclusive deployments are impacting AMD’s financial performance and contributing to its competitive positioning.

• AMD's data center GPU revenue reached $3.7 billion in fiscal year 2026, representing a significant increase and establishing a solid foundation for future growth. This figure demonstrates AMD's growing presence in the AI chip market, driven by increasing demand for its EPYC CPUs and Instinct GPUs [ref_idx 55]. While still significantly lower than NVIDIA's $39.1 billion, AMD's growth trajectory suggests the potential to capture a larger share of the expanding market.

• AMD's revenue growth is attributed to its ability to offer competitive hardware solutions and form strategic partnerships with key players in the hyperscaler market. Meta's exclusive deployment of its Llama-405B model on AMD GPUs highlights the value proposition of AMD's products for demanding AI workloads [ref_idx 77]. These design wins not only contribute to immediate revenue but also enhance AMD's reputation and credibility in the industry.

• AMD's strategic partnerships with hyperscalers, including Microsoft, Oracle Cloud, and IBM Cloud, are crucial for its continued revenue growth. These partnerships provide AMD with access to a broader customer base and enable it to scale its production and distribution capabilities. Furthermore, AMD's focus on open-source software and its ROCm platform appeals to customers seeking alternatives to NVIDIA's CUDA ecosystem [ref_idx 77].

• However, AMD's revenue growth is not without challenges. The company faces intense competition from NVIDIA, which maintains a strong ecosystem advantage and commands premium prices for its GPUs. Additionally, AMD's ability to sustain its growth trajectory depends on its ability to continue innovating and delivering competitive hardware and software solutions. Short term, revenue projections for 2027 depend heavily on smooth launches of the MI400 series and enterprise enablement of the ROCm platform.

• AMD needs to focus on securing more design wins and expanding its partnerships with hyperscalers. AMD could offer attractive pricing and customized solutions to attract customers seeking to diversify their AI hardware suppliers. Long term, sustained growth requires AMD to further develop its software ecosystem, improve its debugging tools, and actively foster developer engagement.

• Analysts project AMD's compound annual growth rate (CAGR) for AI GPUs to exceed 30% between 2026 and 2030, driven by design wins with Azure, Oracle, and IBM. This aggressive growth forecast reflects increasing confidence in AMD’s ability to capture a greater portion of the expanding AI chip market [ref_idx 362]. If AMD can hit its targets, its total revenue could reach $44 billion by 2027 with EPS growing 30% annually.

• This growth projection is further bolstered by AMD's strategic focus on addressing the specific needs of hyperscalers and cloud service providers. By tailoring its products and solutions to meet the unique requirements of these customers, AMD can establish itself as a trusted partner and secure long-term contracts. Recent data shows they are already gaining ground among hyperscalers who see the value in multi-sourcing for both price and supply reasons [ref_idx 77].

• HSBC analysts forecast that the price-to-performance advantage of AMD’s MI355 AI GPU will translate into healthy data center revenue growth. They now expect AMD to achieve $15 billion in AI GPU revenue in 2026, which is higher than the previous consensus estimate of $9.6 billion [ref_idx 364]. This increased revenue projection underscores the potential for AMD to significantly expand its AI GPU business in the coming years.

• However, AMD's ability to achieve this ambitious growth target depends on several factors. The company must effectively execute its product roadmap, maintain its competitive pricing strategy, and continue to build strong relationships with hyperscalers. In the medium term, growing geopolitical tension between the U.S. and China could disrupt supply chains and impact AMD's growth plans [ref_idx 360].

• AMD should prioritize strengthening its partnerships with hyperscalers, focusing on long-term contracts and customized solutions. AMD can offer tiered pricing to hyperscalers, incentivizing volume purchases and securing long-term commitments. AMD should invest in R&D to maintain its competitive edge, particularly in high-performance computing and AI acceleration [ref_idx 363].

• Intel's share in inference-focused GPU segments is shrinking, declining by 5% year-over-year. This trend suggests that Intel is facing challenges in maintaining its competitive position in the AI inference market [ref_idx 55]. While Intel has launched AI chips for data centers and PCs, its market share is being eroded by the strong performance of NVIDIA and the growing presence of AMD.

• Intel's struggles in the inference market stem from several factors, including competition from NVIDIA's CUDA ecosystem and AMD's increasing penetration in the data center. NVIDIA's CUDA platform provides developers with a comprehensive set of tools and libraries for AI inference, making it difficult for Intel to compete [ref_idx 241]. Additionally, AMD's focus on open-source software and its ROCm platform appeals to customers seeking alternatives to NVIDIA's proprietary ecosystem.

• The market share decline puts additional pressure on Intel to develop and deploy competitive inference solutions. Their recently launched Arc Pro B60 and B50 GPUs, designed for AI inference and professional workstations, aim to address this [ref_idx 499]. Early data suggests these have been unsuccessful at moving the needle as analysts reported they are not showing up in market share data. Sustained erosion will further impede growth and pressure other business segments.

• The recent loss is a significant headwind given inference is projected to account for a larger share of AI computing chip demand. While training accounted for 90% of AI computing chip demand in 2023, inference’s share is expected to increase to over 20% by 2025 [ref_idx 78]. To capitalize on this trend, Intel needs to strengthen its competitive positioning by delivering innovative and cost-effective inference solutions.

• Intel should invest in R&D to develop more powerful and efficient inference GPUs. Intel needs to enhance its software ecosystem and provide developers with a comprehensive set of tools and libraries for AI inference. Develop more partnerships with key players in the hyperscaler market to increase adoption of its inference solutions.

• Meta's exclusive deployment of its Llama-405B frontier model on AMD GPUs is projected to generate a significant revenue uplift for AMD, demonstrating the growing demand for AMD's AI chips in hyperscaler deployments. Meta’s move highlights AMD’s hardware as a cost-effective solution with equivalent performance. The move is being watched by other hyperscalers who are keen to break NVIDIA’s dominance.

• The deployment of Llama-405B on AMD's MI300X GPUs enables Meta to deliver enhanced AI capabilities across its social media platforms, including improved AI-powered translations and video generation [ref_idx 527]. These enhancements drive user engagement and advertising revenue, benefiting both Meta and AMD. HSBC expects Meta to be able to support double-digit revenue growth with these types of partnerships.

• Multiple sources confirm that Meta’s 2Q25 earnings benefited significantly from this AI deployment. Meta reported a strong 22% YoY increase in revenue, crediting their ad business’s expansion due to AI technologies [ref_idx 529].

• The relationship between Meta and AMD shows a case study for enterprise adoption. By prioritizing tools, training, and ISV partnerships, AMD can lower enterprise ROCm friction and increase sales. This also increases demand for AMD hardware, increasing AMD’s presence in the market, and their long-term profit potential.

• AMD should continue to nurture its relationship with Meta and other hyperscalers, providing ongoing support and collaboration to ensure successful deployments. AMD could offer customized solutions and services to meet the specific needs of each customer, enhancing its value proposition. Actively engage with the open-source community to foster innovation and build a strong ecosystem around ROCm and its AI chips.

7. Scenario Planning: AMD’s Path to Ecosystem Leadership

• 7-1. Base Case: Slow ROCm Adoption and NVIDIA’s Ecosystem Flywheel

• This subsection initiates scenario planning by establishing a 'Base Case' where AMD struggles to gain significant AI market share due to limited ROCm adoption and NVIDIA's entrenched CUDA ecosystem. It identifies key risks and projects AMD's revenue trajectory under these constraints, providing a grounded perspective for evaluating more optimistic scenarios.

• The AI landscape is currently dominated by NVIDIA's CUDA ecosystem, presenting a significant challenge for AMD's ROCm. CUDA's first-mover advantage has created powerful network effects, evidenced by its widespread use in academic research and industry applications. This dominance is not easily overcome, as it provides a larger pool of skilled developers and readily available resources, making it the default choice for many AI projects. The challenge for AMD lies in incentivizing developers to invest time and resources in learning and utilizing ROCm.

• The core mechanism driving CUDA's continued dominance is its mature and comprehensive toolchain. NVIDIA has invested heavily in libraries, debugging tools, and optimization frameworks that streamline AI development. These resources enable developers to rapidly prototype and deploy AI models, creating a positive feedback loop where increased adoption leads to further investment and improvement. ROCm, while improving, still lags behind in terms of feature completeness and ease of use, creating friction for developers accustomed to CUDA.

• Data from arXiv publications and academic curriculum analysis supports CUDA's deep penetration [ref_idx 64]. CUDA is the de facto standard in approximately 85% of AI research papers due to ease of use and available resources, while ROCm sees niche adoption, primarily within AMD-centric research groups. This imbalance reinforces CUDA's mindshare among emerging AI talent.

• To mitigate this, AMD requires a multi-pronged strategy focused on strategic funding and industry standardization and proactive community building. First, AMD should aggressively target developer retention through increased devrel spending and outreach. Second, AMD needs to drive industry-wide adoption of open standards to minimize CUDA lock-in. The long-term strategic implication is that without drastic action, ROCm's slow adoption will fundamentally cap AMD's ability to gain meaningful traction in the AI market.

• Based on the current slow adoption trajectory, AMD should significantly increase its devrel budget for workshops, cloud credits, and university programs by $400M/year, focusing on ROCm-specific skills and toolchain familiarity. AMD needs to strategically invest in developer-friendly resources and aggressively court academia to foster a robust ROCm ecosystem.

• Under the base case scenario, where ROCm adoption remains sluggish, AMD's ability to capture significant market share in the AI chip market is severely constrained. Despite hardware advancements and competitive pricing, AMD's revenue is projected to be capped at approximately 15% market share by 2030. This projection assumes continued dominance of NVIDIA, fueled by the network effects of its CUDA ecosystem and its established relationships with key hyperscalers.

• The mechanism behind this stagnation lies in the ecosystem-driven advantages of NVIDIA. CUDA's extensive library support, mature toolchain, and large developer base create a powerful flywheel effect, making it difficult for AMD to compete effectively, even with comparable or superior hardware. Furthermore, NVIDIA's pricing power, driven by its perceived ecosystem value, allows it to maintain high margins and reinvest heavily in R&D, further widening the gap.

• AMD's 2024 annual reports and industry analysis point towards a future revenue growth trajectory of 30% CAGR [ref_idx 77]. With NVIDIA projected to capture 85% of the data center revenue, AMD will continue to be a 'distant second' in the AI sector. This will prevent AMD from fully capitalizing on the projected $500 billion AI chip market by 2028 [ref_idx 17].

• This 15% market share cap implies a significant strategic imperative for AMD: it must aggressively disrupt the status quo by attacking NVIDIA's ecosystem advantages directly and capitalize on potential inflection points such as shifts in open-source AI frameworks. AMD needs to increase investment to enable a rapid ecosystem expansion.

• AMD should actively pursue strategic partnerships with key AI framework developers to co-develop ROCm extensions and optimizations. This should include aggressively lowering the switching costs for developers through comprehensive documentation, automated CUDA-to-ROCm conversion tools, and extensive training resources targeting enterprise developers.

• A significant risk factor for AMD in the AI market is NVIDIA's massive R&D budget. NVIDIA's annual R&D spending has grown to nearly double that of AMD [ref_idx 6]. This disparity creates a structural disadvantage for AMD, as it limits the company's ability to innovate and compete on the cutting edge of AI hardware and software.

• The core of NVIDIA’s continued dominance hinges on its ability to continuously invest in and improve its technology. NVIDIA’s enormous R&D budget supports the development of advanced GPU architectures, optimized software libraries, and innovative AI tools, securing its lead in performance and features. The vast financial resources allow NVIDIA to iterate faster and develop AI solutions that AMD will struggle to match.

• Looking at previous fiscal years, NVIDIA's R&D expenditure was reported at $3.090 billion, compared to AMD's $1.583 billion [ref_idx 6]. JP Morgan analysis reinforces this argument. NVIDIA is less exposed to financial risks because its R&D spending relative to cash flow remains stable, allowing it to invest in its AI capabilities [ref_idx 279].

• AMD needs to focus on strategic collaborations and acquisitions to extend R&D capabilities and reduce reliance on organic innovation. They must also increase market share to enable higher revenues to fuel additional R&D spending to close the gap with NVIDIA. In the long term, AMD must find ways to increase its innovation velocity to stay competitive.

• To mitigate this, AMD should strategically target acquisitions of companies with complementary AI technologies and expertise. A focus should also be placed on fostering closer relationships with leading research institutions and universities to leverage external innovation and talent.

• 7-2. High Case: ROCm Enterprise Breakthrough and Open-Source Tidal Wave

• This subsection presents an optimistic 'High Case' scenario for AMD, contingent on a significant breakthrough in enterprise adoption of ROCm and a broader industry shift toward open-source AI frameworks. It quantifies potential cost savings, assesses hyperscaler strategies to hedge against geopolitical risks, and contrasts these prospects with the 'Base Case' to highlight the potential upside for AMD.

• A pivotal factor in AMD's potential to challenge NVIDIA's dominance lies in the enterprise adoption of its ROCm platform. This scenario models a future where 50% of Fortune 500 IT departments actively utilize ROCm for AI workloads by 2028, signifying a major shift in developer mindshare and infrastructure preferences. Such adoption hinges on ROCm demonstrating tangible benefits in terms of cost, flexibility, and performance compared to NVIDIA's CUDA ecosystem.

• The mechanism driving this adoption breakthrough is the diminishing lock-in effect of CUDA and the increasing maturity of ROCm. As open-source frameworks like PyTorch and TensorFlow enhance their support for ROCm, and as AMD invests in developer tools and training resources, the barriers to switching decrease significantly. Furthermore, as enterprises seek to avoid vendor lock-in and reduce dependency on a single supplier, ROCm offers a compelling alternative.

• AMD's acquisition of ZT Systems is crucial for providing full-stack solutions to hyperscalers [ref_idx 18, 31]. These acquisitions signal AMD's commitment to providing enterprise-grade AI solutions. AMD has also partnered with Humain to invest $10B to boost AI by activating multi-exaflop capacity by early 2026 [ref_idx 418]. Absci will receive AMD's Instinct accelerator and ROCm software to boost AI capabilities [ref_idx 248].

• The strategic implication of widespread ROCm adoption is a significant redistribution of market share in the AI chip market. If 50% of Fortune 500 companies embrace ROCm, AMD could capture a substantial portion of AI workloads, eroding NVIDIA's market dominance and driving increased revenue for AMD. This will require that AMD is able to match performance metrics with NVIDIA.

• To achieve this high case adoption rate, AMD needs to focus on several key initiatives including, aggressively expand its ROCm enterprise enablement through strategic ISV partnerships and enhance enterprise support for ROCm. Increase investment in ROCm-specific skills and toolchain familiarity for future workforce.

• A key driver for enterprises considering ROCm adoption is the potential for significant cost savings compared to NVIDIA's premium pricing. This scenario quantifies the total cost of ownership (TCO) savings for a typical Fortune 500 company adopting ROCm over a five-year period, estimating a potential savings of $2 billion or more by avoiding NVIDIA's premium hardware and software licensing costs.

• The primary mechanism behind these savings is the combination of lower acquisition costs for AMD GPUs and reduced operational expenses due to ROCm's open-source nature. NVIDIA's CUDA ecosystem often entails additional licensing fees and proprietary software costs, whereas ROCm's open-source approach eliminates these expenses. Furthermore, AMD's hardware is often competitively priced, offering a compelling alternative to NVIDIA's high-end GPUs.

• According to Andrew Dieckmann, AMD’s general manager for data center GPUs, the company offers meaningful cost savings on both acquisition and operation [ref_idx 18]. AMD is positioning its MI400 and MI355X chips to challenge Nvidia not only on performance but also on price and energy efficiency. A company executive emphasized that AMD’s chips will cost less to operate due to lower power consumption, with “aggressive” pricing strategies to undercut Nvidia.

• This scenario holds significant strategic implications for AMD: aggressive pricing with enterprise-grade support contracts can secure large volume commitments from hyperscalers seeking to reduce costs. The TCO savings underscore the value proposition of ROCm and incentivize enterprises to switch from NVIDIA's ecosystem. AMD’s MI355X chip has demonstrated superior performance in some areas and that open software frameworks have improved significantly, allowing AMD’s hardware to compete effectively [ref_idx 18].

• AMD should aggressively pursue enterprise contracts with tiered pricing, in order to create and emphasize the savings of switching to AMD GPUs. AMD can look to expand strategic partnerships with enterprise software vendors to include ROCm-optimized solutions. AMD can look to use ZT Systems’ acquisition to offer turnkey AI infrastructure solutions to lower enterprise TCO.

• Geopolitical factors, particularly US export bans on advanced AI chips to China, are driving hyperscalers to diversify their GPU sourcing strategies and reduce dependence on a single vendor. This scenario assesses how hyperscalers are implementing multi-vendor hedging strategies to mitigate the impact of potential supply chain disruptions and ensure access to critical AI infrastructure.

• The underlying mechanism for multi-vendor strategies is the desire to maintain operational resilience and competitive advantage in the face of geopolitical uncertainty. Export bans force hyperscalers to seek alternative sources of AI chips, and AMD's ROCm-based GPUs offer a viable option. By supporting multiple GPU architectures, hyperscalers can avoid being overly reliant on a single vendor and maintain flexibility in their AI deployments.

• TensorWave is eager to introduce a “viable alternative” to NVIDIA [ref_idx 31]. As initially reported in the Wall Street Journal on January 8, AMD is also looking to compete against NVIDIA by penetrating specific vertical market segments. As part of this strategy, the company recently invested $20 million in Absci (Nasdaq: ABSI), a public drug discovery company headquartered in Vancouver, Washington [ref_idx 248].

• Diversifying vendors will have a high strategic impact for AMD, if they capitalize by offering readily available alternatives to NVIDIA. The potential shift from cloud providers hedging against US export bans will enable AMD to capture a sizable market share. The collaboration with Aligned and the University of Southern California’s Information Sciences Institute (USC ISI) on the MEGALODON project represents a pivotal moment in its quest to challenge NVIDIA’s dominance in the AI space [ref_idx 103].

• AMD should strengthen its relationships with hyperscalers by offering dedicated support and customized solutions and increase investment in supply chain diversification to ensure stable supply for hyperscalers. In addition, AMD can lobby for favorable policy changes regarding export regulations to reduce market uncertainty.

8. Strategic Recommendations for AMD

• 8-1. Accelerate ROCm Enterprise Enablement

• This subsection delves into actionable strategies for AMD to enhance its ROCm ecosystem, focusing on the critical need for increased developer relations (DevRel) investment. It builds on the previous section's scenario planning by translating potential outcomes into concrete recommendations aimed at fostering enterprise adoption and ultimately driving market share growth.

• AMD's current DevRel budget, estimated at $100M annually, significantly lags behind NVIDIA’s, creating a substantial disadvantage in attracting and retaining developers. While AMD has taken steps to establish a formal DevRel function in January 2025 and sponsor events, these efforts are underfunded relative to the scale of the challenge [ref_idx 6, 66]. To effectively compete with NVIDIA’s CUDA ecosystem, a more aggressive investment is essential.

• A proposed increase to $500M annually, sustained over 3-5 years, would enable AMD to implement comprehensive DevRel programs: (1) Expanded workshops and training sessions targeting enterprise developers, (2) Increased cloud credits for developers to experiment with high-end Instinct GPUs via the AMD Developer Cloud, and (3) Enhanced university programs to foster ROCm expertise among emerging AI talent. This investment should be strategically allocated to address specific pain points in ROCm adoption, such as library coverage and debugging tool gaps [ref_idx 66].

• ZT Systems' acquisition provides a strategic avenue for turnkey AI infrastructure solutions [ref_idx 24]. By bundling hardware and software with enterprise-grade support and training, AMD can lower the barrier to entry for ROCm. This integrated approach requires significant DevRel resources to create documentation, training materials, and support channels. The $500M budget would support these initiatives. Over the short-term (1-2 years), the focus should be on building a robust foundation; medium-term (3-5 years) on scaling adoption; long-term (6-10 years) on sustaining ecosystem growth and innovation.

• The strategic implication of scaling DevRel investment is a shift in the developer mindshare, leading to greater enterprise adoption of ROCm and a reduction in the ecosystem-driven margin advantage currently held by NVIDIA. By providing better tooling, training, and support, AMD can reduce the total cost of ownership (TCO) for enterprises, driving adoption and revenue [ref_idx 18, 31]. We recommend allocating a significant portion of the budget to incentivize ISV partnerships and co-marketing initiatives, demonstrating the value of ROCm to enterprise clients.

• AMD's acquisition of ZT Systems for $4.9 billion signals a strategic move towards offering complete AI infrastructure solutions, positioning itself as a viable alternative to NVIDIA's proprietary AI systems [ref_idx 24, 173]. However, the acquisition's true value hinges on successfully integrating ZT Systems' engineering talent and hyperscaler relationships with AMD's ROCm software ecosystem.

• The integration should focus on pre-validated and optimized ROCm-based solutions tailored to specific enterprise AI workloads, such as generative AI, machine learning, and HPC [ref_idx 24, 107]. ZT Systems can be leveraged to create turnkey AI infrastructure solutions that reduce the complexity and cost associated with ROCm adoption. This can involve pre-integrating ROCm with popular AI frameworks like PyTorch and TensorFlow and providing enterprise-grade support and documentation.

• To fully realize the benefits, AMD must spin off ZT Systems' manufacturing operations, allowing it to maintain strong relationships with its OEM and ODM partners [ref_idx 24]. This will help AMD to concentrate on design and engineering capacities, accelerating the time to market for new AI systems. The near-term (1-2 years) objective should be seamless integration of ZT Systems, in the medium-term (3-5 years) is to establish AMD as a leading provider of turnkey AI solutions, and in the long-term (6-10 years) is to sustain differentiation through ecosystem development and innovation.

• The strategic implication is that by combining hardware and software expertise, AMD can create a more compelling value proposition for enterprises, driving ROCm adoption and market share gains. We recommend prioritizing enterprise enablement efforts and showcasing the benefits of AMD's integrated solutions through case studies and customer testimonials. Key performance indicators (KPIs) should include ROCm adoption rate among Fortune 500 companies and TCO savings for enterprise deployments.

• 8-2. Aggressive Pricing and Hyperscaler Playbook

• Building upon the previous subsection's focus on enterprise enablement, this section outlines a strategic pricing approach coupled with targeted hyperscaler partnerships to drive AMD's market share and revenue growth in the AI GPU market. It transitions from internal ecosystem development to external market strategies, aiming to capitalize on AMD's hardware competitiveness.

• To effectively challenge NVIDIA's dominance, AMD must implement a tiered pricing strategy for the MI400X, particularly when engaging with hyperscalers. While NVIDIA commands premium pricing (e.g., $25,000 for MI355X [ref_idx 391]), AMD can undercut this by offering aggressive volume discounts to secure multi-year contracts. A key element of this strategy involves offering the MI400X at a discounted price point, such as $8,000, for Azure, Oracle, and IBM Cloud for volume purchases, undercutting NVIDIA's offerings and creating a compelling economic incentive [ref_idx 18].

• Modeling a 20% ASP (Average Selling Price) erosion from a hypothetical list price of $10,000, but achieving a 50% unit volume growth, could enable AMD to reach $15B in AI GPU revenue by 2030. This projection assumes that hyperscalers will increasingly adopt AMD GPUs to diversify their vendor base and mitigate supply chain risks [ref_idx 77, 30]. Bank of America Securities analyst Vivek Arya anticipates stronger-than-expected pricing for AMD's current-generation MI355X GPUs, estimating over $20,000 versus consensus estimates of $17,000 [ref_idx 389]. This pricing confidence supports the viability of a tiered pricing model.

• Implementing this pricing strategy requires careful monitoring of NVIDIA’s pricing tactics and continuous optimization of AMD’s cost structure. The near-term (1-2 years) focus should be on securing initial hyperscaler design wins with competitive pricing. Medium-term (3-5 years) efforts should involve scaling production to meet demand and optimizing ASP to maximize profitability. Long-term (6-10 years) strategies should emphasize value-added services and software optimization to maintain a competitive edge. This proactive pricing approach helps in navigating market dynamics and gaining market share [ref_idx 18].

• The strategic implication of this approach is that aggressive pricing will attract hyperscalers, driving volume growth and ecosystem adoption, ultimately challenging NVIDIA’s revenue dominance. AMD must also leverage its ZT Systems acquisition to provide turnkey AI infrastructure solutions, creating a more compelling value proposition for hyperscalers [ref_idx 24]. This integrated approach would enhance the attractiveness of AMD’s solutions by reducing complexity and costs for hyperscaler deployments.

• To level the playing field with NVIDIA, AMD must actively advocate for EU and US subsidies to match NVIDIA’s AI R&D tax breaks. NVIDIA’s substantial R&D investments ($7.3B in 2023 [ref_idx 283], projected to be $16B in 2025 [ref_idx 431]) are enabled by favorable tax incentives, allowing them to maintain a technological edge. AMD needs similar support to sustain its own R&D efforts and compete effectively [ref_idx 424].

• The EU Chips Act, aiming to mobilize over €43 billion in private and public investments by 2030, presents a significant opportunity for AMD to secure funding for its European R&D and manufacturing initiatives [ref_idx 462, 460]. Simultaneously, AMD should lobby for US government support through the CHIPS Act, emphasizing the importance of a diversified AI chip supply chain for national security [ref_idx 472]. AMD can also leverage findings from Morgan Stanley which indicated a sustained market dominance and clear lead against ASICs as a result of R&D and cost efficiency [ref_idx 431].

• Short-term (1-2 years) efforts should focus on building relationships with government officials and demonstrating the economic benefits of supporting AMD’s AI initiatives. Medium-term (3-5 years) strategies should involve establishing R&D partnerships with European and American universities and research institutions to strengthen AMD’s innovation pipeline. Long-term (6-10 years) planning should emphasize creating a sustainable ecosystem that fosters AI innovation and attracts top talent [ref_idx 466].

• The strategic implication is that securing government subsidies will provide AMD with the financial resources necessary to accelerate its R&D, compete with NVIDIA on technology, and contribute to the development of a robust and competitive AI ecosystem. Success requires that AMD presents a compelling case for its role in advancing national AI agendas and fostering economic growth, and also emphasize the need for a diversified supply chain.

9. Conclusion

• This report has analyzed AMD's strategic position in the AI chip market, highlighting its challenges in competing with NVIDIA's dominant CUDA ecosystem and Intel's inference-focused solutions. While AMD possesses competitive hardware and is pursuing aggressive pricing strategies, its success hinges on accelerating ROCm enterprise enablement and securing key hyperscaler partnerships.

• The broader implications of this analysis extend beyond AMD, impacting the overall AI landscape. A more competitive market with multiple strong players fosters innovation, reduces vendor lock-in, and promotes wider accessibility to AI technologies. Geopolitical factors, such as US export bans and government subsidies, further influence the competitive dynamics, underscoring the importance of strategic agility and diversification.

• Looking forward, AMD must prioritize investments in its software ecosystem, foster closer collaborations with memory vendors, and actively engage with policymakers to secure R&D funding. Areas for further research include the impact of emerging technologies like quantum computing on the AI chip market and the effectiveness of AMD's open-source strategy in attracting developers. Ultimately, AMD's ability to address these challenges will determine its role in shaping the future of AI.

• AMD needs to capitalize on the strong demand for AI chips by providing greater memory capacity and similar AI performance at a lower price point. AMD's focus on open-source software and industry standards can be a significant advantage in the long run, attracting developers, researchers, and companies who value openness and interoperability in their AI infrastructure.

Source Documents

• AMD's Software Crisis Analysis - Unlock the GPU - Manifestohttps://unlockgpu.com/reports/gemini/AMDs_Software_Crisis_Analysis.pdf
• AMD's Software Crisis Analysis - Unlock the GPU - Manifestohttps://unlockgpu.com/reports/gemini/AMDs_Software_Crisis_Analysis.pdf
• Could AMD Finally Challenge Nvidia With Its MI400 AI Chips?https://www.nasdaq.com/articles/could-amd-finally-challenge-nvidia-its-mi400-ai-chips
• Project nGene.org® - Industry Insights & Marketing Strategieshttp://www.ngene.org/industry.html
• Could AMD Finally Challenge Nvidia With Its MI400 AI Chips? | The Motley Foolhttps://www.fool.com/investing/2025/06/20/could-amd-finally-challenge-nvidia-with-its-mi400/
• AMD Unveils Next-Generation AI Chips Alongside OpenAI CEO Sam Altmanhttps://digitalmarketreports.com/news/41068/amd-unveils-next-generation-ai-chips-alongside-openai-ceo-sam-altman/
• PDF AMD Acquires ZT Systems - NAND Researchhttps://nand-research.com/wp-content/uploads/2024/08/2024-08-26-RN-AMD-ZT-Systems.pdf
• PDF Analyst Report: NVIDIA Corporation (NVDA)https://tradingadvisorstorage.blob.core.windows.net/pdfs/nvda_2024_12_report_v0.6.pdf
• How AMD Is Gaining Ground Against NVIDIA - Futuriomhttps://www.futuriom.com/articles/news/how-amd-is-gaining-ground-against-nvidia/2025/01
• AMD's AI Chips Gain Ground in Data Centers: A Sign for More Upside? — TradingView Newshttps://www.tradingview.com/news/zacks:4c60f5362094b:0-amd-s-ai-chips-gain-ground-in-data-centers-a-sign-for-more-upside/
• AMD's AI Chips Gain Ground in Data Centers: A Sign for More Upside?https://finance.yahoo.com/news/amds-ai-chips-gain-ground-170000406.html
• AMD's Software Crisis Analysis - Unlock the GPU - Manifestohttps://unlockgpu.com/reports/gemini/AMDs_Software_Crisis_Analysis.pdf
• AMD's Software Crisis Analysis - Unlock the GPU - Manifestohttps://unlockgpu.com/reports/gemini/AMDs_Software_Crisis_Analysis.pdf
• 2024 Annual Report - Investor Relationshttps://ir.amd.com/financial-information/sec-filings/content/0001193125-25-067185/0001193125-25-067185.pdf
• TechGPT: Raising AI revenue forecast by 40% - UBShttps://www.ubs.com/global/en/wealthmanagement/insights/chief-investment-office/house-view/daily/2024/latest-03012024/_jcr_content/root/contentarea/mainpar/toplevelgrid/col_1/textimage_copy_copy_.0237690821.file/dGV4dD0vY29udGVudC9kYW0vYXNzZXRzL3dtL2dsb2JhbC9jaW8vaG91c2Utdmlldy9kb2N1bWVudC9pbmZvcm1hdGlvbi10ZWNobm9sb2d5LWVuLTE2MDYxMDYucGRm/information-technology-en-1606106.pdf
• Generative AI - 2024 - Bloomberg Professional Serviceshttps://assets.bbhub.io/promo/sites/16/Bloomberg-Intelligence-NVDA-Gen-AIs-Disruptive-Race.pdf
• Semiconductors in 2024: Entering a New Era, Propelled by ...https://indexes.nasdaqomx.com/docs/202405%20Semiconductor%20Research%20-%20NQSSSE.pdf
• Global Semiconductor Industry Outlook 2024https://assets.kpmg.com/content/dam/kpmg/sg/pdf/2024/03/global-semiconductor-industry-outlook.pdf
• Global Artificial Intelligence (AI) Chip Market to Worth Over US$ 501.97 Billion By 2033 | Astute Analyticahttps://www.globenewswire.com/news-release/2025/02/19/3028772/0/en/Global-Artificial-Intelligence-AI-Chip-Market-to-Worth-Over-US-501-97-Billion-By-2033-Astute-Analytica.html
• AMD's Strategic Alliances in AI: A Catalyst for Long-Term Growthhttps://www.ainvest.com/news/amd-strategic-alliances-ai-catalyst-long-term-growth-2507/
• Better artificial intelligence stock: Nvidia vs. AMDhttps://www.fool.com.au/2025/06/04/better-artificial-intelligence-stock-nvidia-vs-amd-usfeed/
• 2025-02-04 AMD 4Q 2024 Earnings Call - cloudfront.nethttps://d1io3yog0oux5.cloudfront.net/_b073664ff53f885937de2e2769e34b5c/amd/db/841/9090/webcast_transcript/2025-02-04+AMD+4Q+Earnings+call.pdf
• Micron’s AI Strategy: Analysis of Dominance in Computer Memory and Data Storage - Klover.aihttps://www.klover.ai/micron-ai-strategy-analysis-of-dominance-in-computer-memory-and-data-storage/
• News Posts matching 'HBM4' - TechPowerUphttps://www.techpowerup.com/news-tags/HBM4
• Micron’s HBM3E: Powering the future of AI with high-bandwidth memoryhttps://www.micron.com/about/blog/applications/ai/microns-hbm3e-powering-the-future-of-ai-with-high-bandwidth-memory
• nvidia - Photon Insightshttps://www.photoninsights.com/data/diligence-report.pdf
• The 2 Top AI Hardware Stocks to Buy With $1,000 in 2025: SMCI and AMDhttps://www.ainvest.com/news/2-top-ai-hardware-stocks-buy-1-000-2025-smci-amd-2507/
• The AI Chip Market Explosion: Key Stats on Nvidia, AMD, and Intel’s AI Dominance | PatentPChttps://patentpc.com/blog/the-ai-chip-market-explosion-key-stats-on-nvidia-amd-and-intels-ai-dominance
• Applications Now Open for $60,000 NVIDIA Graduate Fellowship Awardshttps://blogs.nvidia.com/blog/applications-open-graduate-fellowship-awards-2024/
• PDF Considerations for Scaling Gpu-ready Data Centershttps://www.nextdimensioninc.com/wp-content/uploads/2018/07/AI-Considerations-For-Scaling-GPU-Ready-Data-Centers-Whitepaper.pdf
• PDF Supermicro NVIDIA GB200 NVL72 Datasheethttps://www.supermicro.com/datasheet/datasheet_SuperCluster_GB200_NVL72.pdf
• XD 그룹 - XD Thermalhttps://www.xdthermal.com/ko/author/xduser/page/2/
• Amid AI boom, veteran analyst reboots AMD, Supermicro stock price targetshttps://finance.yahoo.com/news/amid-ai-boom-veteran-analyst-171700494.html
• Venice and MI400 headline AMD’s bold 2026 AI push with open interconnect standardshttps://www.techradar.com/pro/amd-gets-ready-for-nvidias-vera-rubin-and-2026-with-432gb-mi400-gpu-monster-paired-with-256-core-epyc-venice-and-i-cant-wait-to-see-the-sparks-fly
• From Chips to Systems: How AI Is Revolutionizing Compute ...https://www.williamblair.com/-/media/downloads/eqr/2025/williamblair_how-ai-is-revolutionizing-compute-and-infrastructure.pdf
• AMD and Absci Collaborate to Revolutionize AI-Driven Drug Discovery – Markethttps://www.hpbl.co.in/news/amd-and-absci-collaborate-to-revolutionize-ai-driven-drug-discovery/
• AMD GPU가 엔비디아를 못 따라잡는 이유 ‘그놈의 SW’ – 바이라인네트워크https://byline.network/2025/01/2-239/
• ROCm 6.1 release highlights — ROCm Documentationhttps://rocm.docs.amd.com/en/docs-6.1.0/about/release-notes.html
• EYE ON THE MARKET • MICHAEL CEMBALEST • JP MORGANhttps://am.jpmorgan.com/content/dam/jpm-am-aem/global/en/insights/eye-on-the-market/a-severe-case-of-covidia-amv.pdf
• Maintaining NVIDIA's Leadership in The AI Accelerator Markethttps://www.mattkong.com/papers/Nvidia-EE.pdf
• Nvidia Vs. AMD Vs. Intel: Which AI Stock Is Best As Competition Heats Up?https://www.forbes.com/sites/investor-hub/article/nvidia-vs-amd-vs-intel-best-ai-stock-to-buy/
• High-Performance Computing for the Age of AI and Cloud ...https://gpltech.com/wp-content/uploads/2025/04/HPC-For-The-Age-of-AI-eBook.pdf
• 중국 반도체산업의 도전과 성과https://signalm.sedaily.com/Common/FileDownload?fileName=2507_%EC%A4%91%EA%B5%AD_%EB%B0%98%EB%8F%84%EC%B2%B4%EC%82%B0%EC%97%85%EC%9D%98_%EB%8F%84%EC%A0%84%EA%B3%BC_%EC%84%B1%EA%B3%BC.pdf&fullPath=/Service/Branch/Signal/Report/2025/07/31/2507_%EC%A4%91%EA%B5%AD_%EB%B0%98%EB%8F%84%EC%B2%B4%EC%82%B0%EC%97%85%EC%9D%98_%EB%8F%84%EC%A0%84%EA%B3%
• GPU Programming - OpenCL vs CUDA vs ROCm 교육 과정https://www.nobleprog.co.kr/cc/gpuprogopenclcudarocm
• 4 trillion NVIDIA, by what means?https://www.moomoo.com/news/post/55326157/4-trillion-nvidia-by-what-means
• AMD now has more compute on the top 500 than Nvidia | Hacker Newshttps://news.ycombinator.com/item?id=42175624
• Got $3,000? 2 Artificial Intelligence (AI) Stocks to Buy and Hold for the Long Termhttps://finance.yahoo.com/news/got-3-000-2-artificial-103500717.html
• 1 Artificial Intelligence (AI) Stock to Buy Before It Jumps 37%, According to a Wall Street Analyst - The Globe and Mailhttps://www.theglobeandmail.com/investing/markets/stocks/AMD/pressreleases/33492807/1-artificial-intelligence-ai-stock-to-buy-before-it-jumps-37-according-to-a-wall-street-analyst/
• 1 Artificial Intelligence (AI) Stock to Buy Before It Jumps 37%, According to a Wall Street Analysthttps://www.aol.com/1-artificial-intelligence-ai-stock-123000792.html
• Comparing the NVIDIA H100, AMD MI300, and othershttps://dolphinstudios.co/comparing-the-ai-chips-nvidia-h100-amd-mi300/
• AMD vs. RIVN vs. UBER: Which Growth Stock Is Wall Street’s Best Pick Ahead of Q2 Earnings? - TipRanks.comhttps://www.tipranks.com/news/amd-vs-rivn-vs-uber-which-growth-stock-is-wall-streets-best-pick-ahead-of-q2-earnings
• NVIDIA A100 Tensor Core GPU Architecturehttps://images.nvidia.com/aem-dam/en-zz/Solutions/data-center/nvidia-ampere-architecture-whitepaper.pdf
• TPU vs. GPU for GNN training - CERN Indicohttps://indico.cern.ch/event/1015407/contributions/4261881/attachments/2209662/3739396/20210316_IML_TPUvsGPU.pdf
• PDF Nvidia Rtx Blackwell Gpu Architecturehttps://images.nvidia.com/aem-dam/Solutions/geforce/blackwell/nvidia-rtx-blackwell-gpu-architecture.pdf
• HIPRT: A Ray Tracing Framework in HIP - AMD GPUOpenhttps://gpuopen.com/download/HIPRT-paper.pdf
• AMD Partners with HUMAIN to Launch $10B to Boost AI. - TechBullionhttps://techbullion.com/amd-partners-with-humain-to-launch-10b-to-boost-ai/
• Nvidia's Unrelenting Leadership and Work Ethic Drive a $4.2 Trillion Market Caphttps://www.ainvest.com/news/nvidia-unrelenting-leadership-work-ethic-drive-4-2-trillion-market-cap-2507/
• NVIDIA Maintains Market Dominance, Morgan Stanley Reportshttps://www.gurufocus.com/news/2702583/nvidia-maintains-market-dominance-morgan-stanley-reports
• A Chips Act for Europe - DECISIONhttp://www.decision.eu/wp-content/uploads/2022/09/A-Chips-Act-for-Europe-Commission-Staff-Working-Document.pdf
• UNIVERSITA' DEGLI STUDI DI PADOVAhttps://thesis.unipd.it/retrieve/7187f87d-1952-46d8-b0fd-08ac194ffe42/Bianco_Claudio.pdf
• Global Value Chain Development Report 2023https://www.wto.org/english/res_e/booksp_e/07_gvc23_ch4_dev_report_e.pdf
• Introduction to PCIe and CXL - CERN Indicohttps://indico.cern.ch/event/1182415/contributions/5227101/attachments/2668199/4624349/Introduction%20to%20PCI%20Express.pdf
• NVIDIA H100 PCIe GPU - Product Briefhttps://www.nvidia.com/content/dam/en-zz/Solutions/gtcs22/data-center/h100/PB-11133-001_v01.pdf
• NVIDIA H100 NVL GPU - Product Briefhttps://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/h100/PB-11773-001_v01.pdf
• SuperServer® - Supermicrohttps://www.supermicro.com/manuals/superstorage/MNL-2638.pdf
• Computex 2025: Intel Unveils New GPUs for AI and Workstations |https://www.gurufocus.com/news/2872705/computex-2025-intel-unveils-new-gpus-for-ai-and-workstations-intc-stock-news?mobile=true
• PDF SSESSING NTEL S ISRUPTIVE TRATEGY - Moor Insights & Strategyhttps://moorinsightsstrategy.com/wp-content/uploads/2024/05/Assessing-Intels-Disruptive-AI-Strategy-FINALv5.29.24.pdf
• Nvidia at the Center of the Generative AI Ecosystem—For Now – Communications of the ACMhttps://cacm.acm.org/opinion/nvidia-at-the-center-of-the-generative-ai-ecosystem-for-now/
• Intel Gaudi 2 crushes Nvidia H100 in AI training; Stable Diffusion 3 runs faster & cheaper, toohttps://mspoweruser.com/intel-gaudi-2-crushes-nvidia-h100-in-ai-training-stable-diffusion-3-runs-faster-cheaper-too/
• Microsoft and Meta: Goldman Sachs Says Buy These ‘Magnificent 7’ Stocks After Earnings - TipRanks.comhttps://www.tipranks.com/news/microsoft-and-meta-goldman-sachs-says-buy-these-magnificent-7-stocks-after-earnings
• Here's Why Meta's Earnings Were Better-Than-Expected, According to CEO Mark Zuckerberghttps://www.entrepreneur.com/business-news/meta-stock-reaches-record-after-reporting-quarterly-earnings/495316
• Measuring GPU Memory Latencyhttps://chipsandcheese.com/p/measuring-gpu-memory-latency