Autor: Jakub Rusinowski · Ostatnia aktualizacja: 20 sierpnia 2024
Yes — comfortably
Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~10.4 GB spare and running at ~95.2 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~95.2 tok/s
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| Usable memory for models | 16 GB |
| Memory bandwidth | 717 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 9.2 GB | ✓ Yes | 64K | ~58.4 tok/s | 7.6 GB |
| Q8_0 | 5.6 GB | ✓ Yes | 64K | ~95.2 tok/s | 4 GB |
| Q6_K | 4.7 GB | ✓ Yes | 64K | ~113.8 tok/s | 3.1 GB |
| Q5_K_M | 4.3 GB | ✓ Yes | 64K | ~125 tok/s | 2.7 GB |
| Q4_K_M | 3.9 GB | ✓ Yes | 128K | ~137.7 tok/s | 2.3 GB |
| Q3_K_M | 3.2 GB | ✓ Yes | 128K | ~166.5 tok/s | 1.6 GB |
| Q2_K | 2.9 GB | ✓ Yes | 128K | ~188 tok/s | 1.2 GB |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~10.4 GB spare and running at ~95.2 tok/s (estimated), with room for about 65,536 tokens of context.
Q8_0 — it needs about 5.6 GB of the 16 GB available, downloads as roughly 4 GB, and runs at an estimated 95.2 tokens/sec with up to 64K 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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