Mixtral 8x22B needs ~65.7 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q2_K quantization, expect ~11 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
32.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.6 tok/s
TTFT
54454 ms
Safe context
4K
Memory
96.7 GB / 64.0 GB
Offload
30%
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.7 tok/s | 28608 ms | 4K |
| Coding | F | Too heavy | 3.6 tok/s | 54454 ms | 4K |
| Agentic Coding | F | Too heavy | 3.3 tok/s | 85213 ms | 4K |
| Reasoning | F | Too heavy | 3.6 tok/s | 64355 ms | 4K |
| RAG | F | Too heavy | 3.3 tok/s | 106516 ms | 4K |
How Mixtral 8x22B (141B params) fits at each quantization level on NVIDIA A16 64GB (64.0 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 |
Copy-paste commands to run Mixtral 8x22B on your machine.
Run
ollama run mixtral:8x22bUpgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickMakes 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 96GBBest valueMakes 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 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.
~$12,000 MSRP
Yes, NVIDIA A16 64GB can run Mixtral 8x22B at Q2_K quantization (Runs with offload (needs ~1.4 GB host RAM)). The recommended Q4_K_M requires 96.7 GB which exceeds available memory, but at Q2_K it needs only 65.7 GB. Expected decode speed: 10.7 tok/s.
Mixtral 8x22B (141B parameters) requires approximately 96.7 GB at Q4_K_M quantization. On NVIDIA A16 64GB, it fits at Q2_K using 65.7 GB.
The recommended quantization is Q4_K_M, but on NVIDIA A16 64GB the best fitting quantization is Q2_K, which uses 65.7 GB.
On NVIDIA A16 64GB, Mixtral 8x22B achieves approximately 10.7 tokens per second decode speed with a time-to-first-token of 18149ms using Q2_K quantization.
For coding workloads, Mixtral 8x22B on NVIDIA A16 64GB receives a F grade with 3.6 tok/s and 4K context.
On NVIDIA A16 64GB, Mixtral 8x22B can safely use up to 8K tokens of context at Q2_K quantization. The model's official context limit is 66K, 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-8x22b-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.