The AMD Instinct MI455X is an enterprise-class GPU built on the CDNA 5 architecture, featuring 432 GB of memory with 19,600 GB/s of memory bandwidth.
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LLMs that fit on the AMD Instinct MI455X
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 432 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| DeepSeek R1 DeepSeek | 671B 37B active | — Not released | 826 GB Too large | 481 GB Tight on 1× · 7% spare |
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB 1× · 240 GB free |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB Too large | 330 GB 1× · 102 GB free | 192 GB 1× · 240 GB free |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB Too large | 310 GB 1× · 122 GB free | 181 GB 1× · 251 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 1× · 150 GB free | 155 GB 1× · 277 GB free | 90 GB 1× · 342 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 1× · 354 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 1× · 346 GB free | 47 GB 1× · 385 GB free | 28 GB 1× · 404 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 1× · 353 GB free | 43 GB 1× · 389 GB free | 25 GB 1× · 407 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 1× · 357 GB free | 41 GB 1× · 391 GB free | 24 GB 1× · 408 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 1× · 357 GB free | 41 GB 1× · 391 GB free | 24 GB 1× · 408 GB free |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 1× · 365 GB free | 37 GB 1× · 395 GB free | 21 GB 1× · 411 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 1× · 374 GB free | 32 GB 1× · 400 GB free | 18 GB 1× · 414 GB free |
Estimates: model weights plus 20% for KV cache and runtime overhead, at short context lengths. Long contexts and large batches need more. Rows marked “tight” hold the weights but not that full margin. A ×N figure is the total VRAM across N of these GPUs and makes no claim about interconnect throughput. How we estimate this · Start from a model instead
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