Mixtral 8x7B needs ~34.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~32 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
2.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.2 GB host RAM)
Decode
32.3 tok/s
TTFT
5988 ms
Safe context
4K
Memory
34.7 GB / 32.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~1.5 GB host RAM) | 33.9 tok/s | 3116 ms | 4K |
| Coding | B | Very compromised (needs ~2.2 GB host RAM) | 32.3 tok/s | 5988 ms | 4K |
| Agentic Coding | B | Very compromised (needs ~3.7 GB host RAM) | 29.5 tok/s | 9533 ms | 4K |
| Reasoning | B | Very compromised (needs ~2.2 GB host RAM) | 32.3 tok/s | 7077 ms | 4K |
| RAG | B | Very compromised (needs ~3.7 GB host RAM) | 29.5 tok/s | 11916 ms | 4K |
How Mixtral 8x7B (47B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | B65 |
Q3_K_SBest for your GPU | 3 | 23.0 GB | Low | B64 |
NVFP4 | 4 | 26.3 GB | Medium | F0 |
Q4_K_M | 4 | 28.7 GB | Medium | F0 |
Q5_K_M | 5 | 33.8 GB | High | F0 |
Q6_K | 6 | 38.5 GB | High | F0 |
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 mixtralUpgrade options
RTX A6000 48GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 30%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 151%.
~$4,999 MSRP
NVIDIA L40 48GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 35%.
~$5,500 MSRP
Yes, NVIDIA V100 32GB can run Mixtral 8x7B with a B grade (Very compromised (needs ~2.2 GB host RAM)). Expected decode speed: 32.3 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 34.7 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 NVIDIA V100 32GB, Mixtral 8x7B achieves approximately 32.3 tokens per second decode speed with a time-to-first-token of 5988ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on NVIDIA V100 32GB receives a B grade with 32.3 tok/s and 4K context.
On NVIDIA V100 32GB, Mixtral 8x7B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-on-v100-32gb" 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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