Can I Run BitNet b1.58 on 256 GB system RAM?

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 204.8 GB usable on 256 GB system RAM, leaving ~199.6 GB spare and running at ~16.3 tok/s (estimated).

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~16.3 tok/s

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256 GB system RAM — what it gives a model

Usable memory for models	204.8 GB
Memory bandwidth	90 GB/s

BitNet b1.58 on 256 GB system RAM: memory by quantization

Quant	Memory needed	Fits 204.8 GB?	Max context	Est. speed	Download
F16	8.3 GB	✓ Yes	—	~9.3 tok/s	6.6 GB
Q8_0	5.2 GB	✓ Yes	—	~16.3 tok/s	3.5 GB
Q6_K	4.4 GB	✓ Yes	—	~20.3 tok/s	2.7 GB
Q5_K_M	4 GB	✓ Yes	—	~22.9 tok/s	2.4 GB
Q4_K_M	3.7 GB	✓ Yes	—	~25.9 tok/s	2 GB
Q3_K_M	3.1 GB	✓ Yes	—	~33.6 tok/s	1.4 GB
Q2_K	2.8 GB	✓ Yes	—	~40 tok/s	1.1 GB

What to watch out for

This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.

Recommended setup

llama.cpp (CPU build) or Ollama — both run without a GPU

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
204.8 GB of the 256 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is inferred from the parameter count because this model does not publish its full attention configuration.

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