作者： Jakub Rusinowski · 最后更新： 2025年3月12日
Yes
Yes — Gemma 3 4B Instruct at Q8_0 needs about 6.2 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) (~1.8 GB spare), at ~38.6 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~38.6 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 | 9.9 GB | ✗ No | — | — | 8 GB |
| Q8_0 | 6.2 GB | ✓ Yes | 16K | ~38.6 tok/s | 4.3 GB |
| Q6_K | 5.2 GB | ✓ Yes | 16K | ~47.3 tok/s | 3.3 GB |
| Q5_K_M | 4.8 GB | ✓ Yes | 16K | ~52.8 tok/s | 2.8 GB |
| Q4_K_M | 4.4 GB | ✓ Yes | 32K | ~59.2 tok/s | 2.4 GB |
| Q3_K_M | 3.6 GB | ✓ Yes | 32K | ~74.6 tok/s | 1.7 GB |
| Q2_K | 3.3 GB | ✓ Yes | 32K | ~87 tok/s | 1.3 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 25.4 GB | ✗ Too large | — |
| Gemma 3 12B Instruct | 11.1 GB | ✗ Too large | — |
| Gemma 3 4B Instruct | 4.4 GB | ✓ Fits | ~59.2 tok/s |
| Gemma 3 1B Instruct | 2 GB | ✓ Fits | ~149.6 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — Gemma 3 4B Instruct at Q8_0 needs about 6.2 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) (~1.8 GB spare), at ~38.6 tok/s (estimated), with room for about 16,384 tokens of context.
Q8_0 — it needs about 6.2 GB of the 8 GB available, downloads as roughly 4.3 GB, and runs at an estimated 38.6 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