FitMyLLM
Mistral AI/Dense

Mistral AIMagistral Small 24B

Mistral's first dedicated reasoning model. Chain-of-thought reasoning under Apache 2.0.

chatreasoningcoding
24B
Parameters
128K
Context length
10
Benchmarks
10
Quantizations
Architecture
Dense
Released
2025-06-01
Layers
40
KV Heads
8
Head Dim
128
Family
mistral

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
14.4 GB
12.5 + 1.9 KV
low
Q3_K_L4.3
15.3 GB
13.4 + 1.9 KV
moderate
IQ4_XS4.46
15.7 GB
13.9 + 1.9 KV
moderate
Q4_K_S4.67
16.4 GB
14.5 + 1.9 KV
moderate
Q4_K_M4.89
17.0 GB
15.2 + 1.9 KV
good
Q5_K_S5.57
19.1 GB
17.2 + 1.9 KV
good
Q5_K_M5.7
19.5 GB
17.6 + 1.9 KV
good
Q6_K6.56
22.0 GB
20.2 + 1.9 KV
excellent
Q8_08.5
27.9 GB
26.0 + 1.9 KV
lossless
FP1616
50.4 GB
48.5 + 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 MAGISTRAL SMALL 24B NOW

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

Community Ratings

Loading ratings...

Benchmarks (10)

AIME70.7
GPQA Diamond68.2
IFEval65.7
LiveCodeBench55.8
BBH52.8
MMLU-PRO49.4
BigCodeBench36.1
MATH35.6
GPQA18.6
MUSR17.1

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run magistral:24b-small-2506-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 Magistral Small 24B

Build Hardware for Magistral Small 24B
▸ SPEC SHEET

Magistral Small 24B24B Dense.

▸ SPECIFICATIONS
PARAMETERS
24B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
128K tokens
CAPABILITIES
chat, reasoning, coding
RELEASE DATE
2025-06-01
PROVIDER
Mistral AI
FAMILY
mistral
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M412.5 GB88%
Q3_K_L4.313.4 GB90%
IQ4_XS4.4613.9 GB92%
Q4_K_S4.6714.5 GB93%
Q4_K_M4.8915.2 GB94%
Q5_K_S5.5717.2 GB96%
Q5_K_M5.717.6 GB96%
Q6_K6.5620.2 GB97%
Q8_08.526.0 GB100%
FP161648.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO49.4
MATH35.6
IFEval65.7
BBH52.8
GPQA18.6
MUSR17.1
BigCodeBench36.1
LiveCodeBench55.8
AIME70.7
GPQA Diamond68.2
§ 02RUN COMMAND

Run Magistral Small 24B locally with Ollama — needs 15.2 GB VRAM at Q4_K_M:

$ollama run magistral:24b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M