Mixtral 8x22B needs ~94.3 GB but NVIDIA A100 40GB only has 40.0 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
54.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.7 tok/s
TTFT
40894 ms
Safe context
4K
Memory
94.3 GB / 40.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 94.3 GB, but this setup only exposes 40.0 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 | 4.7 tok/s | 22306 ms | 4K |
| Coding | F | Too heavy | 4.7 tok/s | 40894 ms | 4K |
| Agentic Coding | F | Too heavy | 4.7 tok/s | 59482 ms | 4K |
| Reasoning | F | Too heavy | 4.7 tok/s | 48329 ms | 4K |
| RAG | F | Too heavy | 4.7 tok/s | 74353 ms | 4K |
How Mixtral 8x22B (141B params) fits at each quantization level on NVIDIA A100 40GB (40.0 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 |
アップグレードオプション
RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視Makes 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.
〜$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良Makes 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.
〜$9,999 MSRP
NVIDIA H20 96GBNVIDIAアップグレードMakes 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.
〜$12,000 MSRP
No, Mixtral 8x22B requires more memory than NVIDIA A100 40GB provides.
Mixtral 8x22B (141B parameters) requires approximately 94.3 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 A100 40GB, Mixtral 8x22B achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 40894ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x22B on NVIDIA A100 40GB receives a F grade with 4.7 tok/s and 4K context.
On NVIDIA A100 40GB, 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-a100-40gb" 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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