Layer 2 · head to head
AMD Instinct MI300X vs NVIDIA H100
The generation where AMD first became a real answer for memory-bound inference.
| AMD Instinct MI300X | NVIDIA H100 | |
|---|---|---|
| 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 HBM3 | 80 GB HBM3 |
| Bandwidth | 5.3 TB/s | 3.35 TB/s |
| Compute | CDNA 3 FP8 | Hopper-generation FP8 |
| Power | 750 W | 700 W |
| Interconnect | Infinity Fabric | NVLink 4 |
| Software | ROCm | CUDA |
| Workload | both | both |
AMD Instinct MI300X
For: The first AMD part that was genuinely competitive for LLM inference, and now the value option in that lane.
The catch: A generation behind MI355X. Worth choosing only on price, and only if ROCm already works for your stack.
Economics: For memory-bound LLM serving it achieves competitive or superior cost per token against H100 at most batch sizes.
NVIDIA H100
For: The reference point everything else is benchmarked against, and still the most rentable accelerator on earth.
The catch: 80 GB is the binding limit. Large models need multi-GPU sharding that a 141 GB or 288 GB part would not, and sharding costs you latency and complexity.
Economics: The benchmark denominator. When a vendor claims '2.6x an H100', this is the H100 they mean.
Before either — can you power it?
750 W against 700 W. 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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