Written by Jakub Rusinowski · Last updated March 1, 2024
Yes — comfortably
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.8 GB spare and running at ~46.1 tok/s (estimated).
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~46.1 tok/s
| 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 | 8.3 GB | ✗ No | — | — | 6.6 GB |
| Q8_0 | 5.2 GB | ✓ Yes | — | ~46.1 tok/s | 3.5 GB |
| Q6_K | 4.4 GB | ✓ Yes | — | ~56.4 tok/s | 2.7 GB |
| Q5_K_M | 4 GB | ✓ Yes | — | ~62.8 tok/s | 2.4 GB |
| Q4_K_M | 3.7 GB | ✓ Yes | — | ~70.4 tok/s | 2 GB |
| Q3_K_M | 3.1 GB | ✓ Yes | — | ~88.3 tok/s | 1.4 GB |
| Q2_K | 2.8 GB | ✓ Yes | — | ~102.7 tok/s | 1.1 GB |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.8 GB spare and running at ~46.1 tok/s (estimated).
Q8_0 — it needs about 5.2 GB of the 8 GB available, downloads as roughly 3.5 GB, and runs at an estimated 46.1 tokens/sec.
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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