Can I Run Ministral on 128 GB system RAM?
Superseded model. Ministral has been superseded by Mistral Small 4. This page is kept for reference; the newer family is a better starting point.
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Written by Jakub Rusinowski · Last updated October 16, 2024
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
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.
FAQ
Can I run Ministral on 128 GB system RAM?
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.
Which quantization of Ministral should I use on 128 GB system RAM?
Q8_0 — it needs about 10.5 GB of the 102.4 GB available, downloads as roughly 8.5 GB, and runs at an estimated 7.2 tokens/sec with up to 32K of context.
What limits Ministral on 128 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 128 GB system RAM
Ministral on GPUs
What This Model Is Good At
Model & Tools
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