Layer 2 · head to head
NVIDIA B200 vs AMD Instinct MI355X
The memory argument, stated cleanly: 192 GB of CUDA against 288 GB of ROCm.
| NVIDIA B200 | AMD Instinct MI355X | |
|---|---|---|
| Can you get it | Rentable by the hour from multiple clouds; purchasable at scale. | Rentable by the hour from multiple clouds; purchasable at scale. |
| Memory | 192 GB HBM3E | 288 GB HBM3E |
| Bandwidth | 8 TB/s | 8 TB/s |
| Compute | FP4 and FP8 sparse; vendor figures vary by configuration | ~10 PFLOPS sparse FP4 |
| Power | Up to 1,000 W per GPU in liquid-cooled configurations | Not consistently published |
| Interconnect | NVLink 5 | Infinity Fabric |
| Software | CUDA | ROCm |
| Workload | both | both |
NVIDIA B200
For: The default frontier accelerator. If you have no specific reason to choose otherwise, this is what the market chose.
The catch: 192 GB is less memory than AMD's MI355X at 288 GB, and for memory-bound inference that gap is the whole argument.
Economics: CUDA maturity is the real product. The chip is competitive; the software moat is why it wins procurement.
AMD Instinct MI355X
For: Memory-bound inference. 288 GB against Blackwell's 192 GB is the cleanest technical argument AMD has made in a decade.
The catch: ROCm, not CUDA. The gap has narrowed sharply but it is still the reason procurement teams say no, and porting cost is real.
Economics: AMD positions this as the best price-per-token part for inference at scale, and claims 2.6x H100 inference throughput on Llama 3.1 405B for the MI350X.
Before either — can you power it?
Up to 1,000 W per GPU in liquid-cooled configurations against Not consistently published. In 2026 that comparison usually matters more than the FLOPS one: the US interconnection queue exceeds 2,600 GW with waits approaching five years, and roughly 80% of projects withdraw before energising.
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