Written by Jakub Rusinowski · Last updated January 15, 2025
Yes
Yes — InternLM 3 8B Instruct at Q8_0 needs about 10.6 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.4 GB spare), at ~26.6 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~26.6 tok/s
| Usable memory for models | 12 GB |
| Memory bandwidth | 360 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 18.8 GB | ✗ No | — | — | 17.6 GB |
| Q8_0 | 10.6 GB | ✓ Yes | 32K | ~26.6 tok/s | 9.4 GB |
| Q6_K | 8.4 GB | ✓ Yes | 32K | ~33.7 tok/s | 7.2 GB |
| Q5_K_M | 7.4 GB | ✓ Yes | 32K | ~38.3 tok/s | 6.2 GB |
| Q4_K_M | 6.5 GB | ✓ Yes | 32K | ~44.1 tok/s | 5.3 GB |
| Q3_K_M | 5 GB | ✓ Yes | 32K | ~59.2 tok/s | 3.8 GB |
| Q2_K | 4.1 GB | ✓ Yes | 32K | ~73 tok/s | 2.9 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| InternLM 3 20B Instruct | 14.5 GB | ✗ Too large | — |
| InternLM 3 8B Instruct | 6.5 GB | ✓ Fits | ~44.1 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — InternLM 3 8B Instruct at Q8_0 needs about 10.6 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.4 GB spare), at ~26.6 tok/s (estimated), with room for about 32,768 tokens of context.
Q8_0 — it needs about 10.6 GB of the 12 GB available, downloads as roughly 9.4 GB, and runs at an estimated 26.6 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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