作者： Jakub Rusinowski · 最后更新： 2024年3月1日
Yes — comfortably
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~10.8 GB spare and running at ~71.2 tok/s (estimated).
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~71.2 tok/s
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| Usable memory for models | 16 GB |
| Memory bandwidth | 448 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 8.3 GB | ✓ Yes | — | ~42.9 tok/s | 6.6 GB |
| Q8_0 | 5.2 GB | ✓ Yes | — | ~71.2 tok/s | 3.5 GB |
| Q6_K | 4.4 GB | ✓ Yes | — | ~85.9 tok/s | 2.7 GB |
| Q5_K_M | 4 GB | ✓ Yes | — | ~94.8 tok/s | 2.4 GB |
| Q4_K_M | 3.7 GB | ✓ Yes | — | ~105.2 tok/s | 2 GB |
| Q3_K_M | 3.1 GB | ✓ Yes | — | ~128.9 tok/s | 1.4 GB |
| Q2_K | 2.8 GB | ✓ Yes | — | ~147.2 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 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~10.8 GB spare and running at ~71.2 tok/s (estimated).
Q8_0 — it needs about 5.2 GB of the 16 GB available, downloads as roughly 3.5 GB, and runs at an estimated 71.2 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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