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

QuantBitsVRAMQuality
Q3_K_M47.5 GBlow
Q3_K_L4.38.0 GBmoderate
IQ4_XS4.468.3 GBmoderate
Q4_K_S4.678.7 GBmoderate
Q4_K_M4.899.0 GBgood
Q5_K_S5.5710.2 GBgood
Q5_K_M5.710.5 GBgood
Q6_K6.5612.0 GBexcellent
Q8_08.515.4 GBlossless
FP161628.5 GBlossless

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

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

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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: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 RTX 3080 10GB
10 GB VRAM • 760 GB/s
NVIDIA
$429
Intel Arc B570
10 GB VRAM • 456 GB/s
INTEL
$219
NVIDIA P102-101
10 GB VRAM • 320 GB/s
NVIDIA
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 RTX 2080 Ti
11 GB VRAM • 616 GB/s
NVIDIA
$350
NVIDIA GTX 1080 Ti
11 GB VRAM • 484 GB/s
NVIDIA
$200
NVIDIA RTX 5070
12 GB VRAM • 672 GB/s
NVIDIA
$549
NVIDIA RTX 4070 Ti
12 GB VRAM • 504 GB/s
NVIDIA
$799
NVIDIA RTX 4070 SUPER
12 GB VRAM • 504 GB/s
NVIDIA
$599
NVIDIA RTX 4070
12 GB VRAM • 504 GB/s
NVIDIA
$549
NVIDIA RTX 3080 Ti
12 GB VRAM • 912 GB/s
NVIDIA
$550
NVIDIA RTX 3080 12GB
12 GB VRAM • 912 GB/s
NVIDIA
$599
NVIDIA RTX 3060 12GB
12 GB VRAM • 360 GB/s
NVIDIA
$329
AMD RX 7700 XT
12 GB VRAM • 432 GB/s
AMD
$449
AMD RX 6700 XT
12 GB VRAM • 384 GB/s
AMD
$344
AMD RX 6750 XT
12 GB VRAM • 432 GB/s
AMD
$299
Intel Arc B580
12 GB VRAM • 456 GB/s
INTEL
$249
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