ToolSite Mistral Medium 3.5 128B VRAM requirements
Mistral Medium 3.5 128B has 127.7B parameters. With an 8K-token context and one request it needs about 82.7 GB of GPU memory at Q4_K_M, 134 GB at FP8 and 265 GB at FP16/BF16. The published weights take 124 GB (FP8). It does not fit on a 24 GB card at Q4_K_M; the smallest single GPU that holds it with an 8K context is the 141 GB H200.
Open Mistral Medium 3.5 128B in the calculator
VRAM by quantization
Weights plus the KV cache for 8,192 tokens in FP16 and the runtime overhead (0.5 GB plus 10%). Each row opens the calculator with that setting. What the GGUF names mean.
| Precision | Weights | Total | Smallest setup |
|---|---|---|---|
| As published (FP8) | 124 GB | 140 GB | H200 |
| FP16 / BF16 | 238 GB | 265 GB | 2 × H200 |
| FP8 / INT8 | 119 GB | 134 GB | H200 |
| INT4 (AWQ / GPTQ) | 63.2 GB | 73.0 GB | A100 / H100 80GB |
| GGUF Q8_0 | 126 GB | 143 GB | B200 |
| GGUF Q6_K | 97.5 GB | 111 GB | H200 |
| GGUF Q5_K_M | 84.3 GB | 96.2 GB | H200 |
| GGUF Q4_K_M | 72.0 GB | 82.7 GB | H200 |
| GGUF Q3_K_M | 58.1 GB | 67.5 GB | A100 / H100 80GB |
| GGUF Q2_K | 49.8 GB | 58.3 GB | A100 / H100 80GB |
KV cache at long context
All 88 layers use full attention. Each extra token of context adds 352 KB of FP16 cache per request. How the KV cache works.
| Context | KV cache, FP16 | KV cache, FP8 | Total at Q4_K_M |
|---|---|---|---|
| 4K tokens | 1.38 GB | 704 MB | 81.2 GB |
| 32K tokens | 11.0 GB | 5.50 GB | 91.8 GB |
| 128K tokens | 44.0 GB | 22.0 GB | 128 GB |
| 256K tokens | 88.0 GB | 44.0 GB | 176 GB |
Which GPUs can run Mistral Medium 3.5 128B
With 8,192 tokens of context. Several GPUs means one tensor-parallel group of 2, 4 or 8 cards.
| GPU | Memory | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 | 12 GB | Needs 8 | Needs more than 8 |
| RTX 4060 Ti 16GB | 16 GB | Needs 8 | Needs more than 8 |
| RTX 3090 / 4090 | 24 GB | Needs 4 | Needs 8 |
| RTX 5090 | 32 GB | Needs 4 | Needs 8 |
| A100 40GB | 40 GB | Needs 4 | Needs 4 |
| Mac, 64 GB unified memory about 75% of it is usable by the GPU by default | 48 GB | Needs 2 | Needs 4 |
| L40S / RTX 6000 Ada | 48 GB | Needs 2 | Needs 4 |
| A100 / H100 80GB | 80 GB | Needs 2 | Needs 2 |
| Mac, 128 GB unified memory about 75% of it is usable by the GPU by default | 96 GB | Fits on one | Needs 2 |
| H200 | 141 GB | Fits on one | Fits on one |
| B200 | 180 GB | Fits on one | Fits on one |
How fast Mistral Medium 3.5 128B writes
Tokens per second for one request with 8,192 tokens of context, estimated from memory bandwidth. A dash means it does not fit on one card. Try other settings in the speed calculator.
| Hardware | Bandwidth | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 12GB | 360 GB/s | — | — |
| RTX 4090 | 1,008 GB/s | — | — |
| RTX 5090 | 1,792 GB/s | — | — |
| M4 Max Mac (128 GB) | 546 GB/s | 3.7–5.1 | — |
| M3 Ultra Mac Studio (512 GB) | 819 GB/s | 5.6–7.6 | 3.4–4.7 |
| H100 SXM | 3,350 GB/s | — | — |
| H200 | 4,800 GB/s | 31–44 | 20–27 |
Longest context on one GPU
How many tokens of context fit on a single card with one request and an FP16 KV cache. "Full" means the model's whole context window fits.
| GPU | Memory | Q4_K_M | Q8_0 | FP8 |
|---|---|---|---|---|
| RTX 3060 | 12 GB | No | No | No |
| RTX 4060 Ti 16GB | 16 GB | No | No | No |
| RTX 3090 / 4090 | 24 GB | No | No | No |
| RTX 5090 | 32 GB | No | No | No |
| A100 40GB | 40 GB | No | No | No |
| Mac, 64 GB unified memory | 48 GB | No | No | No |
| L40S / RTX 6000 Ada | 48 GB | No | No | No |
| A100 / H100 80GB | 80 GB | No | No | No |
| Mac, 128 GB unified memory | 96 GB | 43K | No | No |
| H200 | 141 GB | 162K | 3K | 25K |
| B200 | 180 GB | 256K (full) | 107K | 128K |
Model details
- Parameters
- 127.7B (127,704,210,176)
- Layers
- All 88 layers use full attention
- Attention cache
- 8 KV heads × 128
- Context length
- 262,144 tokens
- Published weights
- 124 GB (FP8)
- On Hugging Face
- mistralai/Mistral-Medium-3.5-128B
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Numbers read from the model files on Hugging Face on .