Devstral Small 2505 24B — VRAM, Speed & Local Setup

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

Model libraryDevstral → Devstral Small 2505 24B

The first-generation Devstral Small (release 2505), and still the local coding model with the clearest published number attached to it: 46.8% on SWE-Bench Verified, which beat the prior open-source state of the art by 6 points at release. Finetuned from Mistral Small 3.1, so it inherits the 128K context. At 24B dense it is light enough for a single RTX 4090 or a 32GB Mac (~14 GB at Q4_K_M). Apache 2.0.

Devstral Small 2505 24B needs about 15 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters24 Billion
Context window128,000
ArchitectureDense Transformer
ProviderMistral AI
LicenceApache 2.0
Specified atQ4_K_M
System RAM32 GB
Record updated2026-08-15

Licence

Apache-2.0commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB). Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantBits/weightWeightsVRAM neededEst. speedFit on 24 GB
Q2_K2.637.9 GB10 GB~74 tok/s (est.)Fits comfortably
Q3_K_M3.4110.2 GB12.4 GB~60 tok/s (est.)Fits comfortably
Q4_K_M4.8314.5 GB16.6 GB~45 tok/s (est.)Fits comfortably
Q5_K_M5.6717 GB19.2 GB~39 tok/s (est.)Fits comfortably
Q6_K6.5619.7 GB21.8 GB~34 tok/s (est.)Tight fit
Q8_08.5025.5 GB27.6 GB~4 tok/s (est.)Offloads to system RAM (slow)
F1616.0048 GB50.1 GB~2 tok/s (est.)Offloads to system RAM (slow)

Want to set your own context length and KV-cache quantization? Use the interactive VRAM calculator.

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Recommended GPU

The cheapest catalogued GPU that runs Devstral Small 2505 24B is the AMD Radeon RX 9060 XT 16GB (16 GB).

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AMD Radeon RX 9060 XT 16GB
16 GB VRAM · 160 W board power
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How to Run Devstral Small 2505 24B

Install Ollama, then run:

ollama run devstral:24b

Weights on Hugging Face: mistralai/Devstral-Small-2505.

Published Benchmark Scores

Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.

BenchmarkScoreProvenance
SWE-Bench Verified46.8 / 100 %reported · https://mistral.ai/news/devstral/

Best for: software engineering, agentic coding, consumer gpu, local inference.

Can I Run Devstral Small 2505 24B on My GPU?

Other Devstral Sizes

Devstral Small 2505 24B — Frequently Asked Questions

How much VRAM does Devstral Small 2505 24B need?
About 15 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Devstral Small 2505 24B run on an RTX 4090 (24 GB)?
Yes. Devstral Small 2505 24B needs about 15 GB at Q4_K_M, inside a 24 GB card, at an estimated 45 tokens/sec.
How do I run Devstral Small 2505 24B locally?
Install Ollama and run `ollama run devstral:24b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Devstral come in?
Devstral-2 123B (75 GB), Devstral-2 22B (Unverified Listing) (14 GB), Devstral Small 2505 24B (15 GB), Devstral Small 2 24B (15 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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