FitMyLLM
Mistral AI/Dense

Mistral AIMinistral 3 14B

Ministral 3 14B — latest small Mistral with vision and coding support.

chatcodingreasoningvisionThinkingTool Use
14B
Parameters
256K
Context length
19
Benchmarks
10
Quantizations
Architecture
Dense
Released
2025-12-02
Layers
40
KV Heads
8
Head Dim
128
Family
mistral

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
9.4 GB
7.5 + 1.9 KV
low
Q3_K_L4.3
9.9 GB
8.0 + 1.9 KV
moderate
IQ4_XS4.46
10.2 GB
8.3 + 1.9 KV
moderate
Q4_K_S4.67
10.5 GB
8.7 + 1.9 KV
moderate
Q4_K_M4.89
10.9 GB
9.0 + 1.9 KV
good
Q5_K_S5.57
12.1 GB
10.2 + 1.9 KV
good
Q5_K_M5.7
12.3 GB
10.5 + 1.9 KV
good
Q6_K6.56
13.8 GB
12.0 + 1.9 KV
excellent
Q8_08.5
17.2 GB
15.4 + 1.9 KV
lossless
FP1616
30.4 GB
28.5 + 1.9 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN MINISTRAL 3 14B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

Loading ratings...

Benchmarks (19)

IFEval70.3
GPQA Diamond57.2
BBH36.1
LiveCodeBench35.1
IFBench32.0
AIME30.0
MATH-50030.0
AA Math30.0
MMLU-PRO28.7
τ²-Bench27.2
SciCode23.6
AA Long Context22.0
MUSR18.4
AA Intelligence16.0
AA Coding10.9
MATH8.5
GPQA7.3
HLE4.6
Terminal-Bench4.5

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run ministral-3:14b-instruct-2512-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA CMP 170HX 10 GB
10 GB VRAM • 1560 GB/s
NVIDIA
NVIDIA CMP 50HX
10 GB VRAM • 560 GB/s
NVIDIA
NVIDIA CMP 90HX
10 GB VRAM • 760 GB/s
NVIDIA
NVIDIA Tesla K40c
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40d
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40m
12 GB VRAM • 288 GB/s
NVIDIA

Find the best GPU for Ministral 3 14B

Build Hardware for Ministral 3 14B

Ministral 3 14B — latest small Mistral with vision and coding support.

▸ SPEC SHEET

Ministral 3 14B14B Dense.

▸ SPECIFICATIONS
PARAMETERS
14B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat, coding, reasoning, vision
RELEASE DATE
2025-12-02
PROVIDER
Mistral AI
FAMILY
mistral
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M47.5 GB88%
Q3_K_L4.38.0 GB90%
IQ4_XS4.468.3 GB92%
Q4_K_S4.678.7 GB93%
Q4_K_M4.899.0 GB94%
Q5_K_S5.5710.2 GB96%
Q5_K_M5.710.5 GB96%
Q6_K6.5612.0 GB97%
Q8_08.515.4 GB100%
FP161628.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO28.7
MATH8.5
IFEval70.3
BBH36.1
GPQA7.3
MUSR18.4
GPQA Diamond57.2
LiveCodeBench35.1
AIME30.0
MATH-50030.0
HLE4.6
AA Intelligence16.0
AA Coding10.9
AA Math30.0
aa_ifbench32.0
aa_terminal_bench4.5
aa_tau227.2
aa_scicode23.6
aa_lcr22.0
§ 02RUN COMMAND

Run Ministral 3 14B locally with Ollama — needs 9.0 GB VRAM at Q4_K_M:

$ollama run ministral-3:14b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M