AMD Instinct MI300X vs NVIDIA H100 (80GB) for Local AI
A computed, spec-by-spec comparison of the AMD Instinct MI300X and the NVIDIA H100 (80GB) for running private local AI. Every value below is derived from catalog specs and our scoring/compatibility engines — figures shown as “to verify” are not yet confirmed.
| AMD Instinct MI300X | NVIDIA H100 (80GB) | |
|---|---|---|
| Local AI Score | 100 /100 | 91 /100 |
| Memory | 192 GB | 80 GB |
| Memory bandwidth | 5,300 GB/s | 3,350 GB/s |
| Approx FP16 | to verify | 990 TFLOPS |
| Category | Datacenter GPUs | Datacenter GPUs |
| Largest model it runs | Qwen3 235B-A22B (MoE) (Q4_K_M) | Qwen2.5 72B (Q4_K_M) |
| Recommended AI Business OS tier | Enterprise | Enterprise |
| Best deployment | Local / on-prem | Local / on-prem |
Highlighted cells indicate the stronger value in that row (higher is better). Scores and model fit are transparent heuristics for relative guidance, not benchmarks.
Bottom line
The AMD Instinct MI300X leads on our computed Local AI Score (100/100 vs the NVIDIA H100 (80GB)'s 91/100), making it the stronger pick for demanding local AI. The AMD Instinct MI300X carries more memory (192GB vs 80GB), so it can hold larger models or more concurrent agents. Its largest comfortably-runnable model is Qwen3 235B-A22B (MoE) (Q4_K_M). The NVIDIA H100 (80GB) remains the leaner, lower-overhead option where its score is enough.
Overall lead by Local AI Score: AMD Instinct MI300X.
Pick the AMD Instinct MI300X if you want the higher Local AI Score (100/100), you need to run models up to Qwen3 235B-A22B (MoE) (Q4_K_M), you want an always-on, on-prem deployment — it suits the Enterprise AI Business OS tier.
Pick the NVIDIA H100 (80GB) if a leaner, lower-cost setup is enough (91/100), you need to run models up to Qwen2.5 72B (Q4_K_M), you want an always-on, on-prem deployment — it suits the Enterprise AI Business OS tier.
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