Mixtral 8x22B needs ~93.5 GB but NVIDIA V100 32GB only has 32.0 GB. Try a smaller quantization or lighter model.
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
61.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.2 tok/s
TTFT
88583 ms
Safe context
4K
Memory
93.5 GB / 32.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 93.5 GB, but this setup only exposes 32.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.2 tok/s | 48318 ms | 4K |
| Coding | F | Too heavy | 2.2 tok/s | 88583 ms | 4K |
| Agentic Coding | F | Too heavy | 2.2 tok/s | 128848 ms | 4K |
| Reasoning | F | Too heavy | 2.2 tok/s | 104689 ms | 4K |
| RAG | F | Too heavy | 2.2 tok/s | 161061 ms | 4K |
How Mixtral 8x22B (141B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 55.0 GB | Low | F0 |
Q3_K_S | 3 | 69.1 GB | Low | F0 |
NVFP4 | 4 | 79.0 GB | Medium | F0 |
Q4_K_M | 4 | 86.0 GB | Medium | F0 |
Q5_K_M | 5 | 101.5 GB | High | F0 |
Q6_K | 6 | 115.6 GB | High | F0 |
Q8_0 | 8 | 150.9 GB | Very High | F0 |
F16 | 16 | 289.0 GB | Maximum | F0 |
Upgrade-Optionen
RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige WahlMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $9,999 MSRP
NVIDIA H20 96GBNVIDIA-UpgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $12,000 MSRP
No, Mixtral 8x22B requires more memory than NVIDIA V100 32GB provides.
Mixtral 8x22B (141B parameters) requires approximately 93.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Mixtral 8x22B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA V100 32GB, Mixtral 8x22B achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 88583ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x22B on NVIDIA V100 32GB receives a F grade with 2.2 tok/s and 4K context.
On NVIDIA V100 32GB, Mixtral 8x22B can safely use up to 4K tokens of context. The model's official context limit is 66K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x22b-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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