Can I Run BitNet b1.58 on 192 GB system RAM?
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 153.6 GB usable on 192 GB system RAM, leaving ~148.4 GB spare and running at ~16.3 tok/s (estimated).
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~16.3 tok/s
192 GB system RAM — what it gives a model
| Usable memory for models | 153.6 GB |
| Memory bandwidth | 90 GB/s |
BitNet b1.58 on 192 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 153.6 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.
- 153.6 GB of the 192 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.
FAQ
Can I run BitNet b1.58 on 192 GB system RAM?
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 153.6 GB usable on 192 GB system RAM, leaving ~148.4 GB spare and running at ~16.3 tok/s (estimated).
Which quantization of BitNet b1.58 should I use on 192 GB system RAM?
Q8_0 — it needs about 5.2 GB of the 153.6 GB available, downloads as roughly 3.5 GB, and runs at an estimated 16.3 tokens/sec.
What limits BitNet b1.58 on 192 GB system RAM?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 192 GB system RAM
BitNet b1.58 on GPUs
What This Model Is Good At
Model & Tools
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