Written by Jakub Rusinowski · Last updated November 26, 2024
Yes, but it is tight
Yes, but it is tight — OLMo 2 13B Instruct at Q4_K_M needs about 15.8 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~54.4 tok/s (estimated), with room for about 4,096 tokens of context.
Confidence: medium · Recommended quantization: Q4_K_M · Estimated speed: ~54.4 tok/s
| Usable memory for models | 16 GB |
| Memory bandwidth | 960 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 34.9 GB | ✗ No | — | — | 27.4 GB |
| Q8_0 | 22.1 GB | ✗ No | — | — | 14.6 GB |
| Q6_K | 18.7 GB | ✗ No | — | — | 11.2 GB |
| Q5_K_M | 17.2 GB | ✗ No | — | — | 9.7 GB |
| Q4_K_M | 15.8 GB | ✓ Yes | 4K | ~54.4 tok/s | 8.3 GB |
| Q3_K_M | 13.4 GB | ✓ Yes | 4K | ~66.6 tok/s | 5.8 GB |
| Q2_K | 12 GB | ✓ Yes | 4K | ~76 tok/s | 4.5 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| OLMo 2 13B Instruct | 15.8 GB | ✓ Fits | ~54.4 tok/s |
| OLMo 2 7B Instruct | 9.5 GB | ✓ Fits | ~88.1 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, but it is tight — OLMo 2 13B Instruct at Q4_K_M needs about 15.8 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~54.4 tok/s (estimated), with room for about 4,096 tokens of context.
Q4_K_M — it needs about 15.8 GB of the 16 GB available, downloads as roughly 8.3 GB, and runs at an estimated 54.4 tokens/sec with up to 4K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving