This page answers Openai Gpt Oss 20B q5_k_m quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Openai Gpt Oss 20B typically needs around 15GB VRAM at Q5_K_M, and 18GB is safer for smoother usage.
Estimated from Q4 and Q8 requirement bounds using midpoint interpolation.
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 | 420 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q4 | 379 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q4 | 272 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q4 | 263 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q4 | 173 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q4 | 165 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q4 | 161 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q4 | 131 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q4 | 125 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q4 | 99 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q4 | 98 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q4 | 91 tok/s | View full compatibility | Buy options |
Openai Gpt Oss 20B at Q5_K_M is estimated to require about 15GB VRAM minimum, with 18GB 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.
Q5_K_M is a balance point between memory usage and quality. If your GPU is below 15GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.