Can I Run Ministral on 128 GB system RAM?

Superseded model. Ministral has been superseded by Ministral 3. This page is kept for reference; the newer family is a better starting point. View Ministral 3 →

作者： Jakub Rusinowski · 最后更新： 2024年10月16日

Yes — comfortably

Yes, comfortably — Ministral 8B at Q8_0 needs about 10.5 GB of the 102.4 GB usable on 128 GB system RAM, leaving ~91.9 GB spare and running at ~7.2 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~7.2 tok/s

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128 GB system RAM — what it gives a model

Usable memory for models	102.4 GB
Memory bandwidth	90 GB/s

Ministral on 128 GB system RAM: memory by quantization

Quant	Memory needed	Fits 102.4 GB?	Max context	Est. speed	Download
F16	18 GB	✓ Yes	32K	~4 tok/s	16 GB
Q8_0	10.5 GB	✓ Yes	32K	~7.2 tok/s	8.5 GB
Q6_K	8.6 GB	✓ Yes	32K	~9.2 tok/s	6.6 GB
Q5_K_M	7.7 GB	✓ Yes	32K	~10.4 tok/s	5.7 GB
Q4_K_M	6.9 GB	✓ Yes	32K	~12 tok/s	4.8 GB
Q3_K_M	5.4 GB	✓ Yes	32K	~16.1 tok/s	3.4 GB
Q2_K	4.6 GB	✓ Yes	32K	~19.8 tok/s	2.6 GB

Which Ministral sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Ministral 8B	6.9 GB	✓ Fits	~12 tok/s
Ministral 3B	3.8 GB	✓ Fits	~24.3 tok/s

What to watch out for

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.
102.4 GB of the 128 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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