This page answers AI Forever Rugpt 3.5 13B q5_k_m quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: AI Forever Rugpt 3.5 13B typically needs around 10GB VRAM at Q5_K_M, and 12GB 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 | 572 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q4 | 517 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q4 | 371 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q4 | 358 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q4 | 236 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q4 | 225 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q4 | 219 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q4 | 178 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q4 | 170 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q4 | 135 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q4 | 134 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q4 | 124 tok/s | View full compatibility | Buy options |
AI Forever Rugpt 3.5 13B at Q5_K_M is estimated to require about 10GB VRAM minimum, with 12GB 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 10GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.