Devstral Small 2 24B Instruct

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👁 huggingface
HuggingFace 👁 ollama
Ollama

343.0KDownloads625LikesNov 2025Released256K tokensContextMistral Research LicenseLicense96 ExceptionalQuality

Devstral Small 2 24B Instruct (24B parameters) requires approximately 18.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 22 GB of VRAM.

Get started

— copy & paste to run locally

Copy-paste commands to run Devstral Small 2 24B Instruct on your machine.

Run

ollama run devstral-small-2

Quick specs

Parameters24B

Architecturedense

Context256K tokens

Modalitytext

Min RAM9.4 GB

Rec. RAM14.6 GB (Q4_K_M)

LicenseMistral Research License

FamilyDevstral Small

✓ Code✓ Reasoning

About this model

Devstral is an agentic LLM for software engineering tasks. Devstral Small 2 excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench.

•Agentic Coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents
•Lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM,...
•Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes
•Context Window: A 256k context window
•Vision Capabilities: Enables the model to analyze images and provide insights based on visual content, in addition to text

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👁 Intel

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Intel Arc Pro B60 24GB~$599 — 18 tok/s

👁 NVIDIA

Best overallS

RTX 5090 32GB~$1,999 — 88 tok/s

Best hardware

Top picks for Devstral Small 2 24B Instruct

👁 NVIDIA

RTX 5090 32GBS

32 GB

👁 NVIDIA

RTX PRO 4500 Blackwell 32GBS

32 GB

AMD Instinct MI100 32GBS

32 GB

👁 NVIDIA

NVIDIA V100 32GBS

32 GB

👁 NVIDIA

NVIDIA A100 40GBS

40 GB

Run this model

Devstral Small 2 24B Instruct on RTX 5090 32GB Devstral Small 2 24B Instruct on RTX PRO 4500 Blackwell 32GB Devstral Small 2 24B Instruct on AMD Instinct MI100 32GB

Quantization options

VRAM estimates by quant level

No hardware detected — fit column shows raw VRAM estimates

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	—
Q3_K_S	3	11.8 GB	Low	—
NVFP4	4	13.4 GB	Medium	—
Q4_K_M	4	14.6 GB	Medium	—
Q5_K_M	5	17.3 GB	High	—
Q6_K	6	19.7 GB	High	—
Q8_0	8	25.7 GB	Very High	—
F16	16	49.2 GB	Maximum	—

Quality benchmarks