Best LLMs to run on AMD MI350X 288GB
datacenter 288 GB HBM3e · 8000 GB/s · 2310 TFLOPS BF16 · updated 2026-10-23
Headline answer
Of 141 models in our catalog, 120 fit on a single AMD MI350X 288GB at FP8, 128 at 4-bit (AWQ) and 114 at BF16 (8K context, batch 1). Largest that fits: DBRX 132B at BF16; Llama-3.3-Nemotron Ultra 253B at FP8; MiniMax-M1 at 4-bit (AWQ). Rental: $4.00/hr catalog estimate.
Change context, concurrency or framework in the calculator → AMD MI350X 288GB full specs
Every model on AMD MI350X 288GB
Fit band and share of VRAM per precision; N× GPUs = minimum count inside one node (tensor parallel); — = does not fit within one 8-GPU node. Speed and $/1M tokens for the highest-quality precision that fits on one GPU (BF16, else FP8, else 4-bit), single stream.
| Model | Params | BF16 | FP8 | 4-bit (AWQ) | tok/s | $ / 1M tok |
|---|---|---|---|---|---|---|
| MiniMax-M1 | 456.0B (45.9B active) | 4× GPUs | 2× GPUs | tight 86% | 128 4-bit | $13.40 |
| MiniMax-Text-01 | 456.0B (45.9B active) | 4× GPUs | 2× GPUs | tight 86% | 128 4-bit | $13.40 |
| LLaMA 3.1 405B | 405.0B | 4× GPUs | 2× GPUs | good 78% | 22 4-bit | $77.43 |
| Llama 4 Maverick (400B) | 400.0B (17.0B active) | 4× GPUs | 2× GPUs | good 75% | 208 4-bit | $8.22 |
| Qwen3.5 397B-A17B | 397.0B (17.0B active) | 4× GPUs | 2× GPUs | good 74% | 209 4-bit | $8.20 |
| Nemotron-4 340B 4K ctx | 340.0B | 4× GPUs | 2× GPUs | optimal 68% | 26 4-bit | $65.81 |
| MiMo V2-Flash | 309.8B (15.0B active) | 4× GPUs | 2× GPUs | optimal 58% | 218 4-bit | $7.85 |
| DeepSeek V4-Flash | 284.0B (13.0B active) | 4× GPUs | 2× GPUs | optimal 54% | 229 4-bit | $7.48 |
| Llama-3.3-Nemotron Ultra 253B | 253.0B | 2× GPUs | tight 92% | optimal 51% | 18 FP8 | $95.59 |
| DeepSeek Coder V2 | 236.0B (21.0B active) | 2× GPUs | tight 85% | optimal 45% | 135 FP8 | $12.64 |
| Qwen3 235B-A22B | 235.0B (22.0B active) | 2× GPUs | tight 85% | optimal 45% | 131 FP8 | $13.03 |
| MiniMax M2.5 | 230.0B (10.0B active) | 2× GPUs | tight 84% | optimal 44% | 195 FP8 | $8.77 |
| MiniMax M2.7 | 230.0B (10.0B active) | 2× GPUs | tight 84% | optimal 44% | 195 FP8 | $8.77 |
| Mixtral 8x22B | 141.0B (39.0B active) | 2× GPUs | optimal 52% | optimal 28% | 90 FP8 | $19.10 |
| DBRX 132B | 132.0B (36.0B active) | tight 94% | optimal 49% | optimal 26% | 56 BF16 | $30.83 |
| Devstral 2 123B | 123.0B | tight 88% | optimal 46% | optimal 26% | 18 BF16 | $93.00 |
| Mistral Large 2 123B | 123.0B | tight 88% | optimal 46% | optimal 25% | 18 BF16 | $93.00 |
| Nemotron-3 Super 120B-A12B | 120.0B (12.0B active) | tight 85% | optimal 44% | optimal 24% | 125 BF16 | $13.70 |
| Mistral Small 4 119B | 119.0B (11.0B active) | tight 84% | optimal 44% | optimal 24% | 132 BF16 | $12.98 |
| GPT OSS 120B | 117.0B (5.1B active) | tight 83% | optimal 43% | optimal 23% | 195 BF16 | $8.79 |
| Llama 4 Scout (109B) | 109.0B (17.0B active) | good 78% | optimal 41% | optimal 23% | 99 BF16 | $17.30 |
| Command R+ 104B | 104.0B | good 74% | optimal 39% | optimal 24% | 22 BF16 | $79.22 |
| Qwen3-Next 80B-A3B | 80.0B (3.0B active) | optimal 57% | optimal 30% | optimal 16% | 234 BF16 | $7.30 |
| Qwen 2 72B | 72.0B | optimal 52% | optimal 28% | optimal 17% | 30 BF16 | $56.47 |
| Qwen 2.5 72B | 72.0B | optimal 52% | optimal 28% | optimal 17% | 30 BF16 | $56.47 |
| CodeLlama 70B | 70.0B | optimal 51% | optimal 27% | optimal 15% | 31 BF16 | $55.20 |
| DeepSeek R1 Distill 70B | 70.0B | optimal 51% | optimal 27% | optimal 16% | 31 BF16 | $55.20 |
| LLaMA 2 70B 4K ctx | 70.0B | optimal 50% | optimal 27% | optimal 15% | 31 BF16 | $55.20 |
| LLaMA 3.1 70B | 70.0B | optimal 51% | optimal 27% | optimal 16% | 31 BF16 | $55.20 |
| LLaMA 3.3 70B | 70.0B | optimal 51% | optimal 27% | optimal 16% | 31 BF16 | $55.20 |
| Llama-3.1-Nemotron-70B | 70.0B | optimal 51% | optimal 27% | optimal 16% | 31 BF16 | $55.20 |
| Nemotron-H 56B Base | 56.0B | optimal 42% | optimal 23% | optimal 14% | 38 BF16 | $45.15 |
| Llama-3.1-Nemotron-51B | 51.0B | optimal 38% | optimal 20% | optimal 12% | 41 BF16 | $41.53 |
| Llama-3.3-Nemotron Super 49B | 49.0B | optimal 36% | optimal 19% | optimal 12% | 43 BF16 | $40.17 |
| Kimi Linear 48B-A3B | 48.0B (3.0B active) | optimal 35% | optimal 18% | optimal 10% | 235 BF16 | $7.28 |
| Mixtral 8x7B | 46.7B (12.9B active) | optimal 34% | optimal 18% | optimal 10% | 119 BF16 | $14.37 |
| Command R 35B | 35.0B | optimal 30% | optimal 18% | optimal 14% | 56 BF16 | $30.45 |
| Qwen3.6 35B-A3B | 35.0B (3.0B active) | optimal 26% | optimal 14% | optimal 8% | 235 BF16 | $7.29 |
| CodeLlama 34B | 34.0B | optimal 26% | optimal 14% | optimal 8% | 58 BF16 | $29.45 |
| Yi-1.5 34B 4K ctx | 34.0B | optimal 25% | optimal 14% | optimal 8% | 58 BF16 | $29.45 |
| DeepSeek R1 Distill 32B | 32.0B | optimal 24% | optimal 14% | optimal 9% | 61 BF16 | $28.01 |
| Nemotron-Terminal-32B | 32.0B | optimal 24% | optimal 14% | optimal 9% | 61 BF16 | $28.01 |
| OLMo-2 32B 4K ctx | 32.0B | optimal 24% | optimal 13% | optimal 8% | 61 BF16 | $28.01 |
| Qwen 2.5 32B | 32.0B | optimal 24% | optimal 14% | optimal 9% | 61 BF16 | $28.01 |
| Qwen 2.5 Coder 32B | 32.0B | optimal 24% | optimal 14% | optimal 9% | 61 BF16 | $28.01 |
| Qwen3 32B | 32.0B | optimal 24% | optimal 14% | optimal 9% | 61 BF16 | $28.01 |
| Nemotron-3 Nano 30B-A3B | 31.6B (3.2B active) | optimal 23% | optimal 13% | optimal 8% | 231 BF16 | $7.42 |
| Muse Glimmer 30B | 30.0B | optimal 22% | optimal 12% | optimal 8% | 64 BF16 | $26.53 |
| Qwen3 30B-A3B | 30.0B (3.0B active) | optimal 22% | optimal 12% | optimal 7% | 234 BF16 | $7.30 |
| Gemma 2 27B | 27.0B | optimal 21% | optimal 12% | optimal 9% | 70 BF16 | $24.48 |
| Gemma 3 27B | 27.0B | optimal 22% | optimal 12% | optimal 9% | 70 BF16 | $24.51 |
| Qwen3.6 27B | 27.0B | optimal 21% | optimal 12% | optimal 8% | 70 BF16 | $24.44 |
| Devstral 24B | 24.0B | optimal 18% | optimal 10% | optimal 7% | 77 BF16 | $22.28 |
| Devstral Small 2 24B | 24.0B | optimal 18% | optimal 10% | optimal 7% | 77 BF16 | $22.28 |
| Magistral Small 24B | 24.0B | optimal 18% | optimal 10% | optimal 7% | 77 BF16 | $22.28 |
| Mistral Small 3.1 24B | 24.0B | optimal 18% | optimal 10% | optimal 7% | 77 BF16 | $22.28 |
| Codestral 22B | 22.0B | optimal 17% | optimal 10% | optimal 6% | 82 BF16 | $20.89 |
| GPT OSS 20B | 21.0B (3.6B active) | optimal 16% | optimal 9% | optimal 6% | 222 BF16 | $7.71 |
| InternLM 2.5 20B | 20.0B | optimal 16% | optimal 9% | optimal 6% | 88 BF16 | $19.44 |
| DeepSeek Coder V2 Lite | 16.0B (2.4B active) | optimal 13% | optimal 7% | optimal 5% | 250 BF16 | $6.85 |
| StarCoder2 15B | 16.0B | optimal 13% | optimal 7% | optimal 5% | 104 BF16 | $16.53 |
| DeepSeek R1 Distill 14B | 14.8B | optimal 12% | optimal 7% | optimal 5% | 109 BF16 | $15.71 |
| Qwen 2.5 14B | 14.8B | optimal 12% | optimal 7% | optimal 5% | 109 BF16 | $15.71 |
| Qwen3 14B | 14.8B | optimal 12% | optimal 7% | optimal 5% | 109 BF16 | $15.71 |
| Phi 3 Medium | 14.0B | optimal 12% | optimal 7% | optimal 5% | 113 BF16 | $15.17 |
| Phi-4 14B | 14.0B | optimal 12% | optimal 7% | optimal 5% | 113 BF16 | $15.17 |
| Qwen 2.5 Coder 14B | 14.0B | optimal 12% | optimal 7% | optimal 5% | 113 BF16 | $15.17 |
| OLMo-2 13B 4K ctx | 13.7B | optimal 12% | optimal 7% | optimal 6% | 113 BF16 | $15.14 |
| LLaMA 2 13B 4K ctx | 13.0B | optimal 12% | optimal 7% | optimal 5% | 117 BF16 | $14.62 |
| Gemma 3 12B | 12.0B | optimal 11% | optimal 7% | optimal 6% | 124 BF16 | $13.80 |
| Mistral Nemo 12B | 12.0B | optimal 10% | optimal 6% | optimal 5% | 125 BF16 | $13.73 |
| Nemotron-Nano-12B-v2 | 12.0B | optimal 10% | optimal 6% | optimal 5% | 124 BF16 | $13.75 |
| StableLM 2 12B 4K ctx | 12.0B | optimal 10% | optimal 6% | optimal 4% | 124 BF16 | $13.74 |
| Falcon3 10B | 10.0B | optimal 9% | optimal 5% | optimal 4% | 139 BF16 | $12.31 |
| GLM-4 9B | 9.0B | optimal 8% | optimal 5% | optimal 4% | 148 BF16 | $11.56 |
| Gemma 2 9B | 9.0B | optimal 9% | optimal 6% | optimal 5% | 147 BF16 | $11.65 |
| Nemotron-Nano-9B-v2 | 9.0B | optimal 8% | optimal 5% | optimal 4% | 147 BF16 | $11.62 |
| Yi-1.5 9B 4K ctx | 9.0B | optimal 8% | optimal 5% | optimal 3% | 148 BF16 | $11.58 |
| InternLM3 8B | 8.8B | optimal 8% | optimal 5% | optimal 4% | 150 BF16 | $11.42 |
| Qwen3-VL 8B | 8.8B | optimal 8% | optimal 5% | optimal 4% | 150 BF16 | $11.42 |
| MiniCPM-V 4.5 | 8.7B | optimal 8% | optimal 5% | optimal 4% | 150 BF16 | $11.38 |
| Ministral-3 8B | 8.5B | optimal 8% | optimal 5% | optimal 4% | 152 BF16 | $11.24 |
| Qwen2.5-VL 7B | 8.3B | optimal 7% | optimal 4% | optimal 4% | 155 BF16 | $11.07 |
| DeepSeek R1 Distill 8B | 8.0B | optimal 7% | optimal 5% | optimal 4% | 157 BF16 | $10.87 |
| Granite 3.3 8B | 8.0B | optimal 7% | optimal 5% | optimal 4% | 157 BF16 | $10.88 |
| LLaMA 3.1 8B | 8.0B | optimal 7% | optimal 5% | optimal 4% | 157 BF16 | $10.87 |
| Ministral 8B | 8.0B | optimal 7% | optimal 5% | optimal 4% | 157 BF16 | $10.88 |
| Qwen3 8B | 8.0B | optimal 7% | optimal 5% | optimal 4% | 157 BF16 | $10.88 |
| Qwen 2.5 7B | 7.6B | optimal 7% | optimal 4% | optimal 4% | 162 BF16 | $10.58 |
| Falcon3 7B | 7.5B | optimal 7% | optimal 4% | optimal 4% | 163 BF16 | $10.48 |
| Phi-3 Small 7B | 7.4B | optimal 7% | optimal 4% | optimal 4% | 164 BF16 | $10.44 |
| InternLM 2.5 7B | 7.0B | optimal 7% | optimal 4% | optimal 3% | 168 BF16 | $10.16 |
| LLaMA 2 7B 4K ctx | 7.0B | optimal 7% | optimal 5% | optimal 4% | 167 BF16 | $10.26 |
| Mistral 7B | 7.0B | optimal 7% | optimal 4% | optimal 3% | 168 BF16 | $10.16 |
| OLMo-2 7B 4K ctx | 7.0B | optimal 7% | optimal 5% | optimal 4% | 167 BF16 | $10.26 |
| Qwen 2 7B | 7.0B | optimal 6% | optimal 4% | optimal 3% | 169 BF16 | $10.14 |
| Qwen 2.5 Coder 7B | 7.0B | optimal 6% | optimal 4% | optimal 3% | 169 BF16 | $10.14 |
| StarCoder2 7B | 7.0B | optimal 6% | optimal 4% | optimal 3% | 169 BF16 | $10.14 |
| Yi-1.5 6B 4K ctx | 6.0B | optimal 6% | optimal 4% | optimal 3% | 182 BF16 | $9.43 |
| Qwen3-VL 4B | 4.4B | optimal 5% | optimal 3% | optimal 3% | 205 BF16 | $8.34 |
| Gemma 3 4B | 4.3B | optimal 5% | optimal 3% | optimal 3% | 208 BF16 | $8.24 |
| Nemotron-Mini-4B 4K ctx | 4.0B | optimal 4% | optimal 3% | optimal 3% | 213 BF16 | $8.02 |
| Qwen3 4B | 4.0B | optimal 5% | optimal 3% | optimal 3% | 213 BF16 | $8.03 |
| Ministral 3B | 3.8B | optimal 4% | optimal 3% | optimal 3% | 218 BF16 | $7.86 |
| Phi 3.5 Mini | 3.8B | optimal 5% | optimal 4% | optimal 3% | 215 BF16 | $7.95 |
| Phi-4 Mini | 3.8B | optimal 4% | optimal 3% | optimal 3% | 217 BF16 | $7.88 |
| Qwen2.5-VL 3B | 3.8B | optimal 4% | optimal 3% | optimal 2% | 219 BF16 | $7.82 |
| Ministral-3 3B | 3.4B | optimal 4% | optimal 3% | optimal 3% | 226 BF16 | $7.59 |
| LLaMA 3.2 3B | 3.2B | optimal 4% | optimal 3% | optimal 3% | 230 BF16 | $7.45 |
| Falcon3 3B | 3.0B | optimal 4% | optimal 3% | optimal 3% | 234 BF16 | $7.30 |
| Qwen 2.5 3B | 3.0B | optimal 4% | optimal 3% | optimal 2% | 235 BF16 | $7.28 |
| Qwen 2.5 Coder 3B | 3.0B | optimal 4% | optimal 3% | optimal 2% | 235 BF16 | $7.28 |
| StarCoder2 3B | 3.0B | optimal 4% | optimal 3% | optimal 2% | 235 BF16 | $7.28 |
| Qwen3-VL 2B | 2.1B | optimal 3% | optimal 2% | optimal 2% | 256 BF16 | $6.68 |
| Granite 3.1 2B | 2.0B | optimal 3% | optimal 2% | optimal 2% | 260 BF16 | $6.58 |
| Qwen3 1.7B | 1.7B | optimal 3% | optimal 2% | optimal 2% | 268 BF16 | $6.38 |
| SmolLM2 1.7B | 1.7B | optimal 3% | optimal 3% | optimal 2% | 267 BF16 | $6.40 |
| StableLM 2 1.6B 4K ctx | 1.6B | optimal 3% | optimal 2% | optimal 2% | 270 BF16 | $6.33 |
| Qwen 2 1.5B | 1.5B | optimal 2% | optimal 2% | optimal 2% | 276 BF16 | $6.21 |
| Qwen 2.5 1.5B | 1.5B | optimal 2% | optimal 2% | optimal 2% | 276 BF16 | $6.21 |
| Qwen 2.5 Coder 1.5B | 1.5B | optimal 2% | optimal 2% | optimal 2% | 276 BF16 | $6.21 |
| LLaMA 3.2 1B | 1.2B | optimal 2% | optimal 2% | optimal 2% | 284 BF16 | $6.03 |
| Falcon3 1B 4K ctx | 1.0B | optimal 2% | optimal 2% | optimal 2% | 292 BF16 | $5.87 |
| Gemma 3 1B | 1.0B | optimal 2% | optimal 2% | optimal 2% | 292 BF16 | $5.85 |
| Qwen3 0.6B | 0.6B | optimal 2% | optimal 2% | optimal 2% | 306 BF16 | $5.59 |
| Qwen 2 0.5B | 0.5B | optimal 2% | optimal 2% | optimal 2% | 312 BF16 | $5.49 |
| Qwen 2.5 0.5B | 0.5B | optimal 2% | optimal 2% | optimal 2% | 312 BF16 | $5.49 |
| Qwen 2.5 Coder 0.5B | 0.5B | optimal 2% | optimal 2% | optimal 2% | 312 BF16 | $5.49 |
| Kimi K3 | 2800.0B (104.0B active) | — | — | 8× GPUs | 82 4-bit, 8× GPUs | $167.96 |
| DeepSeek V4-Pro | 1600.0B (49.0B active) | — | 8× GPUs | 4× GPUs | 88 FP8, 8× GPUs | $155.03 |
| Kimi K2 | 1000.0B (32.0B active) | — | 4× GPUs | 2× GPUs | 125 FP8, 4× GPUs | $54.93 |
| Kimi K2.5 | 1000.0B (32.0B active) | — | 4× GPUs | 2× GPUs | 125 FP8, 4× GPUs | $54.93 |
| GLM-5 744B | 744.0B (40.0B active) | 8× GPUs | 4× GPUs | 2× GPUs | 100 FP8, 4× GPUs | $68.24 |
| GLM-5.1 744B | 744.0B (40.0B active) | 8× GPUs | 4× GPUs | 2× GPUs | 100 FP8, 4× GPUs | $68.24 |
| DeepSeek R1 | 671.0B (37.0B active) | 8× GPUs | 4× GPUs | 2× GPUs | 104 FP8, 4× GPUs | $65.50 |
| DeepSeek R1-0528 | 671.0B (37.0B active) | 8× GPUs | 4× GPUs | 2× GPUs | 104 FP8, 4× GPUs | $65.50 |
| DeepSeek V3 | 671.0B (37.0B active) | 8× GPUs | 4× GPUs | 2× GPUs | 104 FP8, 4× GPUs | $65.50 |
| DeepSeek V3-0324 | 671.0B (37.0B active) | 8× GPUs | 4× GPUs | 2× GPUs | 104 FP8, 4× GPUs | $65.50 |
| DeepSeek V3.2 | 671.0B (37.0B active) | 8× GPUs | 4× GPUs | 2× GPUs | 104 FP8, 4× GPUs | $65.50 |
| Falcon 40B | 40.0B | — | — | — | — | — |
| Falcon 7B | 7.0B | — | — | — | — | — |
Frequently asked questions
What is the largest LLM that fits on a single AMD MI350X 288GB?
At 8K context and batch 1, the largest models in our catalog that fit on an AMD MI350X 288GB (288 GB) are: DBRX 132B (132B) at BF16; Llama-3.3-Nemotron Ultra 253B (253B) at FP8; MiniMax-M1 (456B) at 4-bit (AWQ). Larger models need tensor parallelism across several GPUs or lower-precision weights.
Can an AMD MI350X 288GB run LLaMA 3.3 70B?
at BF16: fits on one AMD MI350X 288GB (51% of VRAM, ~31 tok/s); at FP8: fits on one AMD MI350X 288GB (27% of VRAM, ~57 tok/s); at 4-bit (AWQ): fits on one AMD MI350X 288GB (16% of VRAM, ~97 tok/s). 8K context, batch 1, vLLM — the calculator sizes other contexts and concurrency.
Can an AMD MI350X 288GB serve DeepSeek R1?
at BF16: needs 8× AMD MI350X 288GB; at FP8: needs 4× AMD MI350X 288GB; at 4-bit (AWQ): needs 2× AMD MI350X 288GB. 8K context, batch 1, vLLM — the calculator sizes other contexts and concurrency.
How fast is an AMD MI350X 288GB for LLM inference?
Single-stream decode for LLaMA 3.1 8B at FP8 is about 213 tokens/s on one AMD MI350X 288GB; decode is bound by memory bandwidth (8000 GB/s), so larger models run proportionally slower. Batching raises total throughput far above the single-stream figure.
How much does it cost to rent an AMD MI350X 288GB?
For on-demand rental our catalog estimate is $4.00/hr. The $/1M tokens column above uses that price.
Methodology: every cell comes from the same engine as our GPU Lookup — weights, KV cache (including MLA latent and hybrid / sliding-window layers), activations and framework overhead per model architecture, decode speed from a calibrated memory-bandwidth roofline. Reference scenario: 8K context (4K for models with a shorter native window), batch 1, vLLM, fp16 KV cache. Fit bands: optimal 50–70% of VRAM, good 70–80%, tight 80–95%. Engineering estimates, not a guarantee — validate against your serving framework before buying hardware.