Can I Run InternLM 3 on 256 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2025年1月15日

Yes — comfortably

Yes, comfortably — InternLM 3 20B Instruct at Q4_K_M needs about 14.5 GB of the 204.8 GB usable on 256 GB system RAM, leaving ~190.3 GB spare and running at ~5.2 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: medium · Recommended quantization: Q4_K_M · Estimated speed: ~5.2 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

InternLM 3 on 256 GB system RAM: memory by quantization

Quant	Memory needed	Fits 204.8 GB?	Max context	Est. speed	Download
F16	42.4 GB	✓ Yes	32K	~1.6 tok/s	40 GB
Q8_0	23.6 GB	✓ Yes	32K	~3 tok/s	21.3 GB
Q6_K	18.8 GB	✓ Yes	32K	~3.9 tok/s	16.4 GB
Q5_K_M	16.6 GB	✓ Yes	32K	~4.4 tok/s	14.2 GB
Q4_K_M	14.5 GB	✓ Yes	32K	~5.2 tok/s	12.1 GB
Q3_K_M	10.9 GB	✓ Yes	32K	~7.1 tok/s	8.5 GB
Q2_K	9 GB	✓ Yes	32K	~8.9 tok/s	6.6 GB

Which InternLM 3 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
InternLM 3 20B Instruct	14.5 GB	✓ Fits	~5.2 tok/s
InternLM 3 8B Instruct	6.5 GB	✓ Fits	~11.9 tok/s

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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