FitMyLLM
Mistral AI/Dense

Mistral AIMistral Small 22B

Mistral Small 22B — solid mid-range model for chat and coding.

chatcodingTool Use
22.2B
Parameters
32K
Context length
12
Benchmarks
10
Quantizations
120K
HF downloads
Architecture
Dense
Released
2024-09-18
Layers
56
KV Heads
8
Head Dim
128
Family
mistral

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
14.2 GB
11.6 + 2.6 KV
low
Q3_K_L4.3
15.0 GB
12.4 + 2.6 KV
moderate
IQ4_XS4.46
15.5 GB
12.9 + 2.6 KV
moderate
Q4_K_S4.67
16.1 GB
13.4 + 2.6 KV
moderate
Q4_K_M4.89
16.7 GB
14.1 + 2.6 KV
good
Q5_K_S5.57
18.6 GB
15.9 + 2.6 KV
good
Q5_K_M5.7
18.9 GB
16.3 + 2.6 KV
good
Q6_K6.56
21.3 GB
18.7 + 2.6 KV
excellent
Q8_08.5
26.7 GB
24.1 + 2.6 KV
lossless
FP1616
47.5 GB
44.9 + 2.6 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

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Community Ratings

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Benchmarks (12)

IFEval72.0
HumanEval68.0
BBH64.0
MMLU-PRO48.0
GPQA Diamond38.1
BigCodeBench36.1
MATH35.6
GPQA18.6
MUSR17.1
LiveCodeBench14.1
AIME6.3
HLE4.3

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run mistral-small:22b-instruct-2409-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.

Apple M1 Pro (16GB)
16 GB VRAM • 200 GB/s
APPLE
$999
Apple M2 Pro (16GB)
16 GB VRAM • 200 GB/s
APPLE
$1299
Apple M4 (16GB)
16 GB VRAM • 120 GB/s
APPLE
$499
NVIDIA Tesla T4 16GB
16 GB VRAM • 320 GB/s
NVIDIA
$800
NVIDIA V100 PCIe 16GB
16 GB VRAM • 900 GB/s
NVIDIA
$2000
Apple M1 (16GB)
16 GB VRAM • 68.25 GB/s
APPLE
$699
Apple M2 (16GB)
16 GB VRAM • 100 GB/s
APPLE
$799
Apple M3 (16GB)
16 GB VRAM • 100 GB/s
APPLE
$799
NVIDIA Tesla P100 DGXS
16 GB VRAM • 732 GB/s
NVIDIA
NVIDIA Tesla P100 PCIe 16 GB
16 GB VRAM • 732 GB/s
NVIDIA
NVIDIA Tesla P100 SXM2
16 GB VRAM • 732 GB/s
NVIDIA
NVIDIA Tesla V100 PCIe 16 GB
16 GB VRAM • 897 GB/s
NVIDIA
NVIDIA Tesla V100 SXM2 16 GB
16 GB VRAM • 1130 GB/s
NVIDIA

Find the best GPU for Mistral Small 22B

Build Hardware for Mistral Small 22B

Mistral Small 22B — solid mid-range model for chat and coding.

▸ SPEC SHEET

Mistral Small 22B22.2B Dense.

▸ SPECIFICATIONS
PARAMETERS
22.2B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat, coding
RELEASE DATE
2024-09-18
PROVIDER
Mistral AI
FAMILY
mistral
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M411.6 GB88%
Q3_K_L4.312.4 GB90%
IQ4_XS4.4612.9 GB92%
Q4_K_S4.6713.4 GB93%
Q4_K_M4.8914.1 GB94%
Q5_K_S5.5715.9 GB96%
Q5_K_M5.716.3 GB96%
Q6_K6.5618.7 GB97%
Q8_08.524.1 GB100%
FP161644.9 GB100%
§ 01BENCHMARK SCORES
HumanEval68.0
MMLU-PRO48.0
MATH35.6
IFEval72.0
BBH64.0
GPQA18.6
MUSR17.1
BigCodeBench36.1
LiveCodeBench14.1
AIME6.3
GPQA Diamond38.1
HLE4.3
§ 02RUN COMMAND

Run Mistral Small 22B locally with Ollama — needs 14.1 GB VRAM at Q4_K_M:

$ollama run mistral-small:22b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M