Autor: Jakub Rusinowski · Ostatnia aktualizacja: 27 czerwca 2024
Yes — comfortably
Yes, comfortably — Gemma 2 9B IT at Q8_0 needs about 13.2 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~10.8 GB spare and running at ~55.9 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~55.9 tok/s
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| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 21.6 GB | ✓ Yes | 8K | ~33.5 tok/s | 18 GB |
| Q8_0 | 13.2 GB | ✓ Yes | 8K | ~55.9 tok/s | 9.6 GB |
| Q6_K | 11 GB | ✓ Yes | 8K | ~67.7 tok/s | 7.4 GB |
| Q5_K_M | 10 GB | ✓ Yes | 8K | ~75 tok/s | 6.4 GB |
| Q4_K_M | 9.1 GB | ✓ Yes | 8K | ~83.4 tok/s | 5.4 GB |
| Q3_K_M | 7.5 GB | ✓ Yes | 8K | ~102.9 tok/s | 3.8 GB |
| Q2_K | 6.6 GB | ✓ Yes | 8K | ~118.1 tok/s | 3 GB |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — Gemma 2 9B IT at Q8_0 needs about 13.2 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~10.8 GB spare and running at ~55.9 tok/s (estimated), with room for about 8,192 tokens of context.
Q8_0 — it needs about 13.2 GB of the 24 GB available, downloads as roughly 9.6 GB, and runs at an estimated 55.9 tokens/sec with up to 8K 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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