作者： Jakub Rusinowski · 最后更新： 2024年8月20日
Yes — comfortably
Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.4 GB spare and running at ~41.6 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~41.6 tok/s
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| Usable memory for models | 8 GB |
| Memory bandwidth | 272 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Price | $1,099 (lib/data/laptops.ts (street price), checked 2026-07-06) |
| Quant | Memory needed | Fits 8 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 9.2 GB | ✗ No | — | — | 7.6 GB |
| Q8_0 | 5.6 GB | ✓ Yes | 16K | ~41.6 tok/s | 4 GB |
| Q6_K | 4.7 GB | ✓ Yes | 32K | ~51.3 tok/s | 3.1 GB |
| Q5_K_M | 4.3 GB | ✓ Yes | 32K | ~57.4 tok/s | 2.7 GB |
| Q4_K_M | 3.9 GB | ✓ Yes | 32K | ~64.7 tok/s | 2.3 GB |
| Q3_K_M | 3.2 GB | ✓ Yes | 32K | ~82.2 tok/s | 1.6 GB |
| Q2_K | 2.9 GB | ✓ Yes | 32K | ~96.7 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 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.4 GB spare and running at ~41.6 tok/s (estimated), with room for about 16,384 tokens of context.
Q8_0 — it needs about 5.6 GB of the 8 GB available, downloads as roughly 4 GB, and runs at an estimated 41.6 tokens/sec with up to 16K 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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