Introduction
In the world of AI chips, AMD has fired a direct shot at Nvidia. With the launch of the MI350 Series, especially the headline-grabbing MI355 chip, AMD is no longer playing defense. At its “Advancing AI” event in San Jose, CEO Lisa Su made the company’s ambition crystal clear: AMD wants a bigger slice of the $500 billion AI chip market—and it’s coming for Nvidia’s crown.
Let’s break down what’s at stake, what each chip delivers, and who is better positioned to dominate the next phase of the AI revolution.
Quick Overview:
AMD’s MI355 claims 35x faster performance over the previous MI300 generation.
AMD says MI355 outperforms Nvidia’s B200 and GB200 chips in key AI tasks.
Nvidia still dominates with ~80% market share in AI GPUs.
AMD chips offer competitive performance at lower cost.
Analysts believe MI350 could help AMD gain share in cloud and enterprise AI.
The MI350 Series: AMD’s AI Leap
The MI350 Series, led by the MI355 chip, is AMD’s most aggressive push into AI accelerators to date.
Built on the CDNA 4 architecture and supported by the new ROCm 6.1 software stack, the MI355 is designed for high-performance AI workloads like model training and inference in data centers.
AMD says the MI355 delivers 35 times more performance than the MI300 released last year.
It supports large-scale language models (LLMs), generative AI tools, and cloud AI platforms, including those from Meta, Microsoft, Oracle, and now OpenAI, xAI, and Cohere.
This chip marks a serious improvement—not just in raw power, but in how AMD integrates hardware and software for AI developers.
Nvidia’s B200: The AI Standard
Nvidia’s B200 is part of its Hopper GPU architecture and has been the industry standard for AI training tasks. It powers many of the world’s most advanced AI systems, including those run by OpenAI and Google.
What makes B200 dominant is Nvidia’s CUDA software ecosystem, which has been refined over decades and is deeply embedded in AI development pipelines. Plus, Nvidia offers a complete stack—including networking and AI software libraries—making it the go-to for turnkey AI infrastructure.
But the B200 comes with a cost: it’s expensive, and supply remains tight due to massive global demand.
/techovedas.com/llms-meet-quantum-nvidias-cuda-q-unlocks-efficient-molecule-simulations/
Comparison
| Feature | AMD MI355 | Nvidia B200 |
|---|---|---|
| Architecture | CDNA 4 | Hopper |
| Performance (claimed) | 35x MI300 (AI tasks) | Top-tier AI throughput |
| Software | ROCm 6.1 | CUDA |
| Power Efficiency | High (claims better) | Industry-leading |
| Pricing | Lower (cost-effective) | Premium |
Who Wins? It Depends
If you want full-stack dominance and a mature ecosystem, Nvidia still leads. But if you’re looking for performance-per-dollar, flexibility, and scalability—especially for cloud providers—AMD’s MI350 Series is a serious challenger.
Analysts are already taking note. Evercore ISI raised AMD’s stock target to $144, citing increased visibility in the data center GPU market. Roth Capital went even higher, to $150, based on optimism around AMD’s Helios rack systems powered by MI350.
techovedas.com/800m-blow-amd-stumbles-as-u-s-chip-ban-hits-china-exports
Conclusion: The AI Chip Race Just Got Real
This isn’t a winner-takes-all situation—but Nvidia finally has real competition. With the MI350 Series, AMD has closed the performance gap, improved its software stack, and lined up major clients.
The next AI battle will be fought not just in performance charts, but in pricing, supply, and ecosystem adoption. MI355 vs B200 is just the beginning.
For more of such news and views choose Techovedas! Your semiconductor Guide and Mate!
