AMD Instinct MI300X vs NVIDIA H100 80GB
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 70 open-weight models.
Quick verdict
AMD Instinct MI300X wins for local AI inference. It has 112 GB more VRAM and 58% more memory bandwidth, runs 64 models natively (vs 54), and exclusively fits 10 models the other cannot. Note: AMD Instinct MI300X uses ROCM while NVIDIA H100 80GB uses CUDA — software ecosystem matters for your framework.
Specs comparison
| Spec | AMD Instinct MI300X | NVIDIA H100 80GB |
|---|---|---|
| VRAM | 192 GB | 80 GB |
| Memory type | HBM3 | HBM3 |
| Bandwidth | 5300 GB/s(+58%) | 3350 GB/s |
| Architecture | CDNA 3 | Hopper |
| Backend | ROCM | CUDA |
| Tier | Datacenter | Datacenter |
| Released | 2023 | 2022 |
| Models (native) | 64 | 54 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | AMD Instinct MI300X | NVIDIA H100 80GB | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 37.9 t/s(FP16) | 63.8 t/s(Q6_K) | -41% |
| Qwen 3.6 27B(27B) | 98.1 t/s(FP16) | 62 t/s(FP16) | +58% |
| Llama 3.1 8B Instruct(8B) | 331.3 t/s(FP16) | 209.4 t/s(FP16) | +58% |
| Qwen 2.5 7B Instruct(7.6B) | 348.7 t/s(FP16) | 220.4 t/s(FP16) | +58% |
Delta is AMD Instinct MI300X relative to NVIDIA H100 80GB.
Only AMD Instinct MI300X can run(10)
Only NVIDIA H100 80GB can run(0)
No exclusive models — AMD Instinct MI300X can run everything NVIDIA H100 80GB can.
Both run natively(54)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Mixtral 8x22B Instruct v0.1149.5 t/svs236.2 t/s
- Qwen 3.5 122B-A10B (MoE)583 t/svs737 t/s
- Nemotron 3 Super 120B485.8 t/svs614.2 t/s
- GPT-OSS 120B1166 t/svs1474 t/s
- Llama 4 Scout 109B342.9 t/svs433.5 t/s
- GLM-4.5 Air 106B485.8 t/svs491.3 t/s
- GLM-4.6V 106B485.8 t/svs491.3 t/s
- Qwen 2.5 72B Instruct36.8 t/svs62 t/s
- Llama 3.3 70B Instruct37.9 t/svs63.8 t/s
- DeepSeek R1 Distill Llama 70B37.9 t/svs63.8 t/s
- Llama 3.1 70B Instruct37.9 t/svs63.8 t/s
- Mixtral 8x7B Instruct v0.1226 t/svs285.7 t/s
- Command-R 35B75.7 t/svs95.7 t/s
- Qwen 3.5 35B-A3B (MoE)971.7 t/svs1228.3 t/s
- Qwen 3.6 35B75.7 t/svs95.7 t/s
- Yi 1.5 34B Chat77 t/svs97.4 t/s
- +38 more on both
Which should you choose?
- • You need to run larger models (>80 GB VRAM)
- • Faster token generation is the priority
- • You want the newer architecture and longer driver support lifecycle
- • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
Frequently asked questions
- Which is better for local AI, the AMD Instinct MI300X or NVIDIA H100 80GB?
- For local AI inference, the AMD Instinct MI300X has the edge. It offers 192 GB VRAM (vs 80 GB) and 5300 GB/s bandwidth (vs 3350 GB/s), letting it run 64 models natively in VRAM vs 54 for its rival.
- How much VRAM does the AMD Instinct MI300X have vs the NVIDIA H100 80GB?
- The AMD Instinct MI300X has 192 GB of HBM3 at 5300 GB/s. The NVIDIA H100 80GB has 80 GB of HBM3 at 3350 GB/s. The AMD Instinct MI300X has 112 GB more VRAM, allowing it to run 10 models the NVIDIA H100 80GB cannot fit natively.
- Can the AMD Instinct MI300X run Llama 3.3 70B?
- Yes. The AMD Instinct MI300X runs Llama 3.3 70B natively at FP16 quantization at approximately 37.9 tokens per second.
- Can the NVIDIA H100 80GB run Llama 3.3 70B?
- Yes. The NVIDIA H100 80GB runs Llama 3.3 70B natively at Q6_K quantization at approximately 63.8 tokens per second.
- What is the difference between the AMD Instinct MI300X and NVIDIA H100 80GB for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The AMD Instinct MI300X has 192 GB VRAM at 5300 GB/s (ROCM backend). The NVIDIA H100 80GB has 80 GB VRAM at 3350 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The AMD Instinct MI300X runs 64 models natively vs 54 for the NVIDIA H100 80GB.