Only with CPU offload
Ministral 3 14B at Q4_K_M needs 9.7 GB but only 7.0 GB is addressable, so about 34% of the layers would stream from system RAM at roughly 60 GB/s. Expect around 11 tokens per second — usable for batch work, painful for chat.
66% on GPU
8K context
Q4_K_M · 7.8 GB
Apache 2.0
Released Dec 2025
Vision
The largest Ministral. A 12 GB card runs it at Q4 with a few gigabytes to spare.
What hardware do I need for Ministral 3 14B? →
Fits instead: Ministral 3 8B (6.7 GB) · Ministral 3 3B (3.6 GB)
The VRAM budget
weights 7.8 GB
Weights 7.8 GB
KV cache @ 8K 1.25 GB
Runtime overhead 0.6 GB
Over budget 2.7 GB past 7.0 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| F16 | 25.9 GB | 27.7 GB | — | ~1.7 | Reference | 20.7 GB over |
| Q8_0 | 13.8 GB | 15.6 GB | — | ~3.9 | −0.1% ppl | 8.6 GB over |
| Q6_K | 10.6 GB | 12.5 GB | — | ~5.9 | −0.4% ppl | 5.5 GB over |
| Q5_K_M | 9.2 GB | 11.0 GB | — | ~7.7 | −0.8% ppl | 4.0 GB over |
| Q4_K_M | 7.8 GB | 9.7 GB | — | ~11 | −1.9% ppl | 2.7 GB over |
| Q3_K_M | 6.3 GB | 8.2 GB | — | ~19 | −5.4% ppl | 1.2 GB over |
| Q2_K | 5.4 GB | 7.3 GB | 6K | ~38 | −15% ppl | 0.3 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.
How to run it
$ llama-server \
-hf mistralai/Ministral-3-14B-Instruct-2512:Q4_K_M \
-c 8192 -ngl 26
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.
01Download is 7.8 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.
03If it falls back to CPU silently, drop the context first, then step down a quantisation.