This page answers Mistralai Mixtral 8x22b Instruct V0 1 q6_k quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Mistralai Mixtral 8x22b Instruct V0 1 typically needs around 20GB VRAM at Q6_K, and 24GB is safer for smoother usage.
Estimated between Q4 and Q8 using a weighted interpolation toward Q8 memory footprint.
Throughput data below uses available compatibility measurements/estimates and is sorted by tokens per second for this model.
Need general guidance? Review full methodology.
| GPU | VRAM | Quantization | Speed | Compatibility | Buy |
|---|---|---|---|---|---|
| AMD Instinct MI300X | 192GB | Q8 | 382 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q8 | 345 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q8 | 248 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q8 | 239 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q8 | 157 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q8 | 150 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q8 | 146 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q8 | 119 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q8 | 114 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q8 | 90 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q8 | 89 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q8 | 83 tok/s | View full compatibility | Buy options |
Mistralai Mixtral 8x22b Instruct V0 1 at Q6_K is estimated to require about 20GB VRAM minimum, with 24GB recommended for smoother operation.
Start with AMD Instinct MI300X, NVIDIA H200 SXM 141GB, NVIDIA H100 SXM5 80GB and review each compatibility page for full speed and fit details.
Q6_K is a balance point between memory usage and quality. If your GPU is below 20GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.