Can I Run Codestral on 192 GB system RAM?

Superseded model. Codestral has been superseded by Devstral. This page is kept for reference; the newer family is a better starting point. View Devstral →

作者： Jakub Rusinowski · 最后更新： 2024年5月29日

Yes — comfortably

Yes, comfortably — Codestral 22B at Q3_K_M needs about 12.1 GB of the 153.6 GB usable on 192 GB system RAM, leaving ~141.5 GB spare and running at ~6.4 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~6.4 tok/s

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192 GB system RAM — what it gives a model

Usable memory for models	153.6 GB
Memory bandwidth	90 GB/s

Codestral on 192 GB system RAM: memory by quantization

Quant	Memory needed	Fits 153.6 GB?	Max context	Est. speed	Download
F16	47.1 GB	✓ Yes	32K	~1.5 tok/s	44.4 GB
Q8_0	26.3 GB	✓ Yes	32K	~2.7 tok/s	23.6 GB
Q6_K	20.9 GB	✓ Yes	32K	~3.5 tok/s	18.2 GB
Q5_K_M	18.4 GB	✓ Yes	32K	~4 tok/s	15.7 GB
Q4_K_M	16.1 GB	✓ Yes	32K	~4.6 tok/s	13.4 GB
Q3_K_M	12.1 GB	✓ Yes	32K	~6.4 tok/s	9.5 GB
Q2_K	10 GB	✓ Yes	32K	~8 tok/s	7.3 GB

What to watch out for

Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
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 computed from this model's published attention configuration.

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