Can I Run Ministral 3 on 24 GB system RAM?
作者： Jakub Rusinowski · 最后更新： 2026年9月6日
Yes — comfortably
Yes, comfortably — Ministral 3 14B at Q6_K needs about 13.7 GB of the 19.2 GB usable on 24 GB system RAM, leaving ~5.5 GB spare and running at ~5.5 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~5.5 tok/s
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24 GB system RAM — what it gives a model
| Usable memory for models | 19.2 GB |
| Memory bandwidth | 90 GB/s |
Ministral 3 on 24 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 19.2 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 30.2 GB | ✗ No | — | — | 28 GB |
| Q8_0 | 17.1 GB | ✓ Yes | 16K | ~4.3 tok/s | 14.9 GB |
| Q6_K | 13.7 GB | ✓ Yes | 32K | ~5.5 tok/s | 11.5 GB |
| Q5_K_M | 12.1 GB | ✓ Yes | 32K | ~6.2 tok/s | 9.9 GB |
| Q4_K_M | 10.6 GB | ✓ Yes | 32K | ~7.2 tok/s | 8.5 GB |
| Q3_K_M | 8.1 GB | ✓ Yes | 64K | ~9.9 tok/s | 6 GB |
| Q2_K | 6.8 GB | ✓ Yes | 64K | ~12.3 tok/s | 4.6 GB |
Which Ministral 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Ministral 3 14B | 10.6 GB | ✓ Fits | ~7.2 tok/s |
| Ministral 3 8B | 6.8 GB | ✓ Fits | ~12.1 tok/s |
| Ministral 3 3B | 3.5 GB | ✓ Fits | ~28.2 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.
- 19.2 GB of the 24 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 Ministral 3 on 24 GB system RAM?
Yes, comfortably — Ministral 3 14B at Q6_K needs about 13.7 GB of the 19.2 GB usable on 24 GB system RAM, leaving ~5.5 GB spare and running at ~5.5 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Ministral 3 should I use on 24 GB system RAM?
Q6_K — it needs about 13.7 GB of the 19.2 GB available, downloads as roughly 11.5 GB, and runs at an estimated 5.5 tokens/sec with up to 32K of context.
What limits Ministral 3 on 24 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 24 GB system RAM
- Mistral Family on 24 GB system RAM
- Mistral Small 3.1 on 24 GB system RAM
- Mistral Small 3.2 on 24 GB system RAM
- Nemotron 3 Nano Omni on 24 GB system RAM
- Nemotron Cascade 2 on 24 GB system RAM
Ministral 3 on GPUs
- Ministral 3 on NVIDIA GeForce RTX 5080
- Ministral 3 on NVIDIA GeForce RTX 5070 Ti
- Ministral 3 on NVIDIA GeForce RTX 5070
- Ministral 3 on NVIDIA GeForce RTX 5060 Ti 16GB
What This Model Is Good At
Model & Tools
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