This page answers Mistralai Mixtral 8x22b Instruct V0 1 q3_k_s 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 8GB VRAM at Q3_K_S, and 10GB is safer for smoother usage.
Estimated from Q4 using a 28% memory reduction assumption for Q3_K_S.
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 | Q4 | 546 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q4 | 493 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q4 | 354 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q4 | 341 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q4 | 225 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q4 | 214 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q4 | 209 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q4 | 170 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q4 | 162 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q4 | 129 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q4 | 128 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q4 | 118 tok/s | View full compatibility | Buy options |
Mistralai Mixtral 8x22b Instruct V0 1 at Q3_K_S is estimated to require about 8GB VRAM minimum, with 10GB 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.
Q3_K_S is a balance point between memory usage and quality. If your GPU is below 8GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.