Mixtral 8x7B needs ~51.0 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~483 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
483.4 tok/s
TTFT
401 ms
Safe context
33K
Memory
51.0 GB / 192.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 483.4 tok/s | 350 ms | 33K |
| Coding | B | Runs well | 483.4 tok/s | 401 ms | 33K |
| Agentic Coding | B | Runs well | 483.4 tok/s | 583 ms | 33K |
| Reasoning | B | Runs well | 483.4 tok/s | 473 ms | 33K |
| RAG | B | Runs well | 483.4 tok/s | 728 ms | 33K |
Inference speed
Estimated decode speed (tokens/sec) for Mixtral 8x7B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~85 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 84.5 | Fits |
| 48 GB | Q4_K_M | 77.1 | Fits | |
| 48 GB | Q4_K_M | 66.0 | Fits | |
| 48 GB | Q4_K_M | 58.1 | Fits | |
| 32 GB | Q4_K_M | 54.9 | Heavy offload | |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 40.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 33.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 31.6 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 24.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 24.7 | Tight |
| 24 GB | Q4_K_M | 19.6 | Too big | |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 18.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 17.3 | Tight |
| 24 GB | Q4_K_M | 16.7 | Too big | |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 15.8 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 13.6 | Offloads |
| 16 GB | Q4_K_M | 7.0 | Too big | |
| 12 GB | Q4_K_M | 4.1 | Too big | |
| 12 GB | Q4_K_M | 2.6 | Too big | |
| 8 GB | Q4_K_M | 2.1 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Mixtral 8x7B (47B params) fits at each quantization level on B100 192GB (192.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | C53 |
Q3_K_S | 3 | 23.0 GB | Low | C53 |
NVFP4 | 4 | 26.3 GB | Medium | C54 |
Q4_K_M | 4 | 28.7 GB | Medium | C54 |
Q5_K_M | 5 | 33.8 GB | High | C55 |
Q6_K | 6 | 38.5 GB | High | B55 |
Q8_0 | 8 | 50.3 GB | Very High | B56 |
F16Best for your GPU | 16 | 96.4 GB | Maximum | B62 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralYes, B100 192GB can run Mixtral 8x7B with a B grade (Runs well). Expected decode speed: 483.4 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 51.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.
On B100 192GB, Mixtral 8x7B achieves approximately 483.4 tokens per second decode speed with a time-to-first-token of 401ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on B100 192GB receives a B grade with 483.4 tok/s and 33K context.
On B100 192GB, Mixtral 8x7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-on-b100-192gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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