AMD MI350 and the GPU competition landscape

Updated December 11, 2025

December 2025 Update: OpenAI taking up to 10% stake in AMD to secure 6GW of GPU supply. MI350 shipping with 288GB HBM3e (vs Blackwell's 180GB), 8TB/s bandwidth. Microsoft Azure running production Copilot workloads on MI300X. Oracle deploying 16,384-GPU MI300X superclusters. AMD's ROCm software stack reaching enterprise maturity as NVIDIA alternatives gain credibility.

OpenAI announced a partnership with AMD that includes taking up to a 10% stake in the company to secure supply of up to six gigawatts of GPUs.¹ AMD signed a deal in October 2025 to supply AI chips to Oracle for its cloud infrastructure.² Microsoft Azure now runs both proprietary and open-source models in production on AMD Instinct MI300X.³ While NVIDIA maintains 80% to 95% of the AI GPU market, AMD established itself as the credible alternative that enterprises increasingly consider for inference workloads and cost optimization.⁴

The MI350 series launched in Q3 2025 with specifications that challenge NVIDIA's Blackwell on paper: 288 gigabytes of HBM3e memory, 8 terabytes per second bandwidth, and claims of 2.2x AI performance over competitive accelerators.⁵ The question for enterprise infrastructure planners is whether AMD's hardware advantages and improving software stack justify the shift from NVIDIA's established ecosystem.

MI350 specifications and positioning

The AMD Instinct MI350 series features 185 billion transistors and 288 gigabytes of HBM3e memory.⁶ Built on 4th generation AMD CDNA architecture, the MI350 series delivers expanded datatype support including MXFP6 and MXFP4 for AI inference, training, and HPC workloads.⁷ The flagship MI355X platform delivers up to 4x peak theoretical performance over the previous generation MI300X.⁸

Memory capacity provides AMD's clearest hardware advantage. The MI355X's 288 gigabytes of HBM3e exceeds NVIDIA's Hopper H200 at 141 gigabytes and the Blackwell B200 at 180 gigabytes.⁹ Memory bandwidth reaches 8 terabytes per second compared to H200's 4.8 terabytes per second and B200's 7.7 terabytes per second.¹⁰

Power consumption reaches 1,400 watts for the MI355X, matching Blackwell Ultra's requirements.¹¹ The similar power profiles mean infrastructure requirements do not differ substantially between vendors at this performance tier.

AMD tested the MI355X against NVIDIA B200 and GB200 platforms, measuring training throughput for fine-tuning Llama2-70B and inference throughput on Llama 3.1-405B.¹² The benchmarks show competitive performance, though real-world results depend heavily on software optimization.

The MI350 shipped to partners and hyperscale data centers in Q3 2025.¹³ AMD's annual accelerator refresh cycle continues with the MI400 series confirmed for 2026 development.¹⁴ The Helios AI reference design integrates MI400 GPUs, EPYC Venice CPUs, and Pensando Vulcano NICs in a full-rack architecture.¹⁵

Cloud provider adoption accelerates

IBM Cloud will add AMD Instinct MI300X GPUs in the first half of 2025.¹⁶ The collaboration enables support for AMD accelerators within IBM's watsonx AI platform and Red Hat Enterprise Linux AI inferencing.¹⁷ The enterprise focus targets customers seeking alternatives to NVIDIA for production AI workloads.

Microsoft Azure launched MI300X-backed AI clusters in Sweden and Ireland regions to support custom Copilot workloads.¹⁸ Microsoft running AMD in production for proprietary models demonstrates that software maturity reached enterprise requirements.

Oracle Cloud Infrastructure's Compute Supercluster instance supports up to 16,384 MI300X GPUs in a single cluster.¹⁹ The scale enables training and deployment of models with hundreds of billions of parameters.²⁰ Oracle's deployment focuses on healthcare and genomic AI use cases where AMD's memory capacity provides advantages.²¹

Vultr and Oracle Cloud wins demonstrate growing momentum behind AMD's accelerator technology.²² Lenovo, Dell, and SuperMicro announced MI300-based offerings.²³ The vendor ecosystem now supports AMD at enterprise scale.

Cohere deploys its Command models on AMD Instinct MI300X, powering enterprise-grade LLM inference with high throughput and data privacy.²⁴ The adoption by AI model providers validates AMD's position for inference workloads.

Software ecosystem matures

The software ecosystem historically limited AMD adoption. CUDA's entrenchment made NVIDIA the default choice. The situation changed substantially in 2025.

PyTorch 3.1 offers native ROCm support for training and inference.²⁵ Popular libraries including DeepSpeed and Hugging Face Accelerate added AMD-specific performance flags.²⁶ Developers are increasingly comfortable building directly for MI300X environments.²⁷

Enterprise AI teams migrate inference workloads to AMD to reduce costs without sacrificing performance.²⁸ The cost differential matters more for inference than training because inference runs continuously and dominates long-term spending.

NVIDIA's CUDA still provides wider developer adoption and more mature tooling.²⁹ Real-world performance in production environments often favors NVIDIA due to ecosystem optimization rather than raw hardware capability.³⁰ Organizations must weigh the cost savings against the engineering investment required to optimize for AMD.

AMD's acquisition of AI hardware and software engineers from Untether AI enhances compiler, kernel development, and chip design capabilities.³¹ The investment strengthens AMD's position in the inference market where CUDA's moat narrows.³²

Market dynamics and share

NVIDIA maintains 80% to 95% of the AI GPU market in 2025.³³ Data from Wells Fargo shows NVIDIA's share in AI accelerators remains between 80% and 90%.³⁴ NVIDIA holds over 90% share in the data center GPU space, with most foundational AI code built on CUDA.³⁵

AMD's data center revenue in Q3 2025 reached $4.3 billion.³⁶ NVIDIA's single-quarter data center revenue by the end of July 2025 reached $41.1 billion.³⁷ The revenue gap demonstrates the scale difference between market leaders.

JPR data shows NVIDIA controls 94% of the discrete GPU market while AMD controls about 6%.³⁸ AMD's share remains a distant second, though the market is expanding rapidly enough that both vendors grow.

AMD's market share in datacenter AI GPUs increased steadily since Q1 2023.³⁹ In Q1 2025, NVIDIA's massive Blackwell ramp commenced, and with AMD's answer only arriving in Q3 2025, AMD's share dipped temporarily.⁴⁰ The competitive cycle will continue as each vendor releases new generations.

Strategic opportunities for AMD

AMD carved a niche in the inference market where NVIDIA's CUDA moat is narrower.⁴¹ Inference will eventually become larger than training, positioning AMD for the market's long-term growth trajectory.⁴²

AMD's approach focuses on strategically selected opportunities rather than attempting to match NVIDIA across all segments.⁴³ The strategy grows AMD's bite of a rapidly expanding market while avoiding direct competition where NVIDIA's advantages are strongest.⁴⁴

The OpenAI partnership represents a major validation. OpenAI's potential $200 billion commitment for up to six gigawatts of AMD GPUs signals confidence in AMD's roadmap.⁴⁵ The deal provides AMD with a marquee customer that influences enterprise perceptions.

AMD's aggressive pricing strategy undercuts NVIDIA, though pricing alone has not enabled AMD to match NVIDIA's performance in market share gains.⁴⁶ The combination of competitive hardware, improving software, and favorable pricing creates opportunities with cost-conscious enterprises.

Enterprise deployment considerations

Organizations evaluating AMD should consider their workload mix. Training workloads, particularly those with extensive CUDA dependencies, still favor NVIDIA. Inference workloads offer more opportunity for AMD adoption with lower switching costs.

Memory capacity advantages matter for large models. The MI350's 288 gigabytes enables single-GPU processing of models that require multiple NVIDIA GPUs. The memory advantage reduces infrastructure complexity for organizations running the largest models.

Software investment requirements should not be underestimated. While ROCm improved substantially, teams accustomed to CUDA will require time and resources to optimize for AMD. The learning curve affects time-to-production for new deployments.

Multi-vendor strategies provide risk mitigation. Organizations that qualify both NVIDIA and AMD can negotiate better pricing, avoid supply constraints, and choose optimal hardware for each workload type. The investment in supporting both platforms pays off for large deployments.

Cloud-based AMD access reduces adoption barriers. IBM, Microsoft, Oracle, and other providers offer AMD instances that enable testing without hardware procurement. Organizations can validate AMD performance on their workloads before committing to infrastructure purchases.

Quick decision framework

AMD vs NVIDIA Selection:

Specification Comparison:

Key takeaways

For infrastructure architects: - AMD MI350 offers 288GB HBM3e—60% more than B200's 180GB - ROCm software stack matured substantially in 2025—PyTorch 3.1 offers native support - Inference workloads offer lowest switching costs from NVIDIA - Cloud providers (IBM, Azure, Oracle) enable testing without hardware procurement

For procurement teams: - OpenAI's 10% AMD stake signals long-term supply confidence - AMD pricing undercuts NVIDIA but hasn't translated to equivalent market share gains - Multi-vendor strategy enables better negotiation leverage and supply resilience - Memory capacity advantage enables single-GPU processing of larger models

For strategic planning: - NVIDIA maintains 80-95% market share—AMD is credible alternative, not replacement - Inference market will eventually exceed training—AMD's target segment - Software investment required for AMD optimization—factor into TCO analysis - MI400 series confirmed for 2026—roadmap visibility improves planning

AMD will remain a distant second to NVIDIA for the foreseeable future.⁴⁷ However, the large and growing AI market means that even a minority share represents substantial revenue and establishes AMD as a viable enterprise option. Organizations that develop AMD expertise position themselves for cost optimization and supply diversification as the market evolves.

References

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SEO Elements

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