Mixtral 8x7B needs ~36.6 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~57 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
56.6 tok/s
TTFT
3419 ms
Safe context
33K
Memory
36.6 GB / 48.0 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 | A | Runs well | 56.6 tok/s | 1865 ms | 33K |
| Coding | A | Runs well | 56.6 tok/s | 3419 ms | 33K |
| Agentic Coding | A | Runs well | 56.6 tok/s | 4973 ms | 33K |
| Reasoning | A | Runs well | 56.6 tok/s | 4041 ms | 33K |
| RAG | A | Runs well | 56.6 tok/s | 6216 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 |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 77.1 | Fits |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 66.0 | Fits |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 58.1 | Fits |
| RTX 5090 32GB | 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 |
| RTX 4090 24GB | 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 |
| RTX 3090 24GB | 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 |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 7.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 4.1 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.6 | Too big |
| RTX 4060 8GB | 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 RTX 6000 Ada 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | B61 |
Q3_K_S | 3 | 23.0 GB | Low | B63 |
NVFP4 | 4 | 26.3 GB | Medium | B64 |
Q4_K_M | 4 | 28.7 GB | Medium | B63 |
Q5_K_M | 5 | 33.8 GB | High | B63 |
Q6_KBest for your GPU | 6 | 38.5 GB | High | B63 |
Q8_0 | 8 | 50.3 GB | Very High | F0 |
F16 | 16 | 96.4 GB | Maximum | F0 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 2.5 VL 72B | 72B | A | 11.1 tok/s | |
| Qwen3-Coder-Next | 80B | A | 28.5 tok/s | |
 Llama 3.3 70B | 70B | A | 11.9 tok/s | |
 Kimi Linear 48B A3B | 48B | S | 26.9 tok/s | |
| Llama 3.1 70B | 70B | B | 11.9 tok/s |
Yes, RTX 6000 Ada 48GB can run Mixtral 8x7B with a A grade (Runs well). Expected decode speed: 56.6 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 36.6 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 RTX 6000 Ada 48GB, Mixtral 8x7B achieves approximately 56.6 tokens per second decode speed with a time-to-first-token of 3419ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on RTX 6000 Ada 48GB receives a A grade with 56.6 tok/s and 33K context.
On RTX 6000 Ada 48GB, 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-rtx-6000-ada-48gb" 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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