This page answers Openai Community Gpt2 Large q6_k quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Openai Community Gpt2 Large typically needs around 2GB VRAM at Q6_K, and 3GB 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 | 641 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q8 | 579 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q8 | 416 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q8 | 401 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q8 | 264 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q8 | 252 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q8 | 245 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q8 | 200 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q8 | 191 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q8 | 151 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q8 | 150 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q8 | 139 tok/s | View full compatibility | Buy options |
Openai Community Gpt2 Large at Q6_K is estimated to require about 2GB VRAM minimum, with 3GB 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 2GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.