Written by Jakub Rusinowski · Last updated January 15, 2025
Yes — comfortably
Yes, comfortably — InternLM 3 20B Instruct at Q8_0 needs about 23.6 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~8.4 GB spare and running at ~53.6 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~53.6 tok/s
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 42.4 GB | ✗ No | — | — | 40 GB |
| Q8_0 | 23.6 GB | ✓ Yes | 32K | ~53.6 tok/s | 21.3 GB |
| Q6_K | 18.8 GB | ✓ Yes | 32K | ~66.5 tok/s | 16.4 GB |
| Q5_K_M | 16.6 GB | ✓ Yes | 32K | ~74.7 tok/s | 14.2 GB |
| Q4_K_M | 14.5 GB | ✓ Yes | 32K | ~84.6 tok/s | 12.1 GB |
| Q3_K_M | 10.9 GB | ✓ Yes | 32K | ~109 tok/s | 8.5 GB |
| Q2_K | 9 GB | ✓ Yes | 32K | ~129.5 tok/s | 6.6 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| InternLM 3 20B Instruct | 14.5 GB | ✓ Fits | ~84.6 tok/s |
| InternLM 3 8B Instruct | 6.5 GB | ✓ Fits | ~157.5 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — InternLM 3 20B Instruct at Q8_0 needs about 23.6 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~8.4 GB spare and running at ~53.6 tok/s (estimated), with room for about 32,768 tokens of context.
Q8_0 — it needs about 23.6 GB of the 32 GB available, downloads as roughly 21.3 GB, and runs at an estimated 53.6 tokens/sec with up to 32K 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
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