Nvidia (NASDAQ: NVDA) has long been the undisputed leader in GPUs, especially in AI workloads. While its dominance won’t disappear overnight, the latest MLPerf Inference v4.1 benchmarks from MLCommons suggest the competition is tightening.
AMD (NASDAQ: AMD), once on the brink of bankruptcy, has not only rebounded but also overtaken Intel in data center CPUs. Now, with its push into GPU accelerators, AMD is beginning to challenge Nvidia’s edge in AI inference performance.
AMD vs. Nvidia in AI #
For years, Nvidia has outperformed AMD in both gaming and data center GPUs, maintaining a strong performance and efficiency lead. However, AMD’s success with its EPYC CPU line has allowed it to reinvest heavily in GPU research, resulting in progress with the MI300X accelerator.
While Nvidia still dominates in AI training, the more lucrative market lies in inference workloads—where trained models are deployed to make predictions. Here, AMD is starting to show real progress.
The Importance of AI Inference #
AI training involves teaching a model with massive datasets. Inference, on the other hand, applies the trained model to unseen data—like making weather forecasts.
As Intel CEO Pat Gelsinger once noted: only a few organizations can train models like weather simulations, but hundreds of millions of people use the predictions daily. That makes inference workloads the bigger commercial opportunity.
MLPerf Benchmark Results #
AMD MI300X vs. Nvidia H100 #
The MI300X is AMD’s flagship AI accelerator, and in MLPerf v4.1 it demonstrated performance nearly equal to Nvidia’s H100 80GB GPU on inference tasks.
- Server inference mode (closest to real-world usage): MI300X token throughput matches H100.
- Offline inference mode: AMD also delivered competitive performance.
This is significant because Nvidia’s advantage has historically been overwhelming.
Memory Advantage of MI300X #
AMD’s MI300X features 192GB of HBM3 memory, compared to Nvidia’s H100 (80GB) and H200 (141GB).
For large AI models like Llama 2 70B, which were used in the benchmarks, this extra memory could provide a real-world advantage not fully reflected in the results.
Nvidia’s Counter with H200 and Blackwell #
Nvidia countered by emphasizing performance against its newer H200 GPU and the upcoming Blackwell B200 chip.
- B200 benchmark: 10,755 tokens/sec in server mode on Llama 2-70B.
- Performance uplift: ~4× faster than H100/MI300X, ~2.5× faster than H200.
However, these gains may come with much higher pricing and power requirements.
Pricing and Value Strategy #
AMD is following the same strategy it used against Intel in CPUs: aggressive pricing.
- Nvidia enjoys gross margins above 50%.
- AMD undercuts with better price-to-performance, potentially attracting cost-sensitive customers.
As seen in CPUs, this strategy could erode Nvidia’s market dominance over time.
The Software Advantage: CUDA vs. ROCm #
Hardware is only half the battle. Nvidia’s CUDA platform remains the industry standard for AI development.
- CUDA’s ecosystem is vast, sticky, and trusted.
- AMD’s alternative, ROCm, has improved but adoption remains limited.
Until this changes, Nvidia’s software moat will protect its market share.
Future Outlook: AMD vs. Nvidia #
Looking ahead:
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Nvidia:
- Strong momentum with Blackwell GPUs.
- High margins and entrenched CUDA ecosystem.
- Risks include delays or design flaws in increasingly complex chips.
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AMD:
- Launching MI325X in Q4 with 288GB of HBM3E memory (more than B200’s 192GB).
- Continues to improve inference performance and memory capacity.
- Market share still small, leaving large upside potential.
Both companies benefit from sustained demand as cloud providers and enterprises scale AI infrastructure.
Conclusion #
- Nvidia remains the leader, especially in AI training and with its unmatched CUDA ecosystem.
- AMD is closing the gap in AI inference, offering competitive hardware with aggressive pricing.
- Nvidia’s Blackwell generation raises the bar, but AMD’s MI325X and future MI350X could further challenge its lead.
From an investment perspective:
- Nvidia is still a strong buy for its dominant position and software moat.
- AMD offers a more attractive risk-reward profile due to its smaller market share and greater upside potential.
The GPU accelerator market will remain highly profitable, and the AMD vs. Nvidia battle will define the next decade of AI computing. 🚀