Devstral Small 2 24B
By Mistral AI · France
Updated 2026-07-13
Overview
Mistral AI's 24B coding specialist co-developed with All Hands AI, scoring 72.2% on SWE-Bench under Apache 2.0. Fits on a single RTX 4090.
When to pick this model
- Single-GPU coding agents on a 4090
- Repository-scale refactoring up to 256K tokens
- SWE-Bench-style autonomous coding tasks
- Apache-licensed commercial code tools
- European-lab-sourced coding infrastructure
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 14 GB |
| Q5_K_M | 17 GB |
| Q8_0 | 26 GB |
| FP16 (no quantization) | 48 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Devstral Small 2 24B needs a 16 GB card at Q4_K_M (14 GB). Stepping up to Q8_0 nearly doubles the footprint to 26 GB, and unquantized FP16 weights take 48 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Devstral Small 2 24B needs roughly 24 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 4 tokens/sec on entry-level GPUs, on the order of 15 tokens/sec on a mid-range card, and up to 40 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Devstral Small 2 24B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Devstral Small 2 24B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 14 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 14 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q4_K_M (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (17 GB used) |
| 32 GB | RTX 5090 | Q8_0 (26 GB used) |
Which GPU should you buy to run Devstral Small 2 24B?
To run Devstral Small 2 24B locally at Q4, you need ~14 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| SWE-Bench | 72.2 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- 72.2% SWE-Bench in a 24B dense model
- Runs comfortably on a single RTX 4090
- 256K context for whole-repo work
- Apache 2.0 license
- Co-developed with All Hands AI for agent workloads
Limitations
- No vision capability
- Specialized for code, weaker as a general assistant
Typical workloads
In our catalog grid, Devstral Small 2 24B is filed under Local Copilot, Code Agents, Refactor — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); French-language output where quality matters.
The 250k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense 24B · Mistral base · 256k ctx · code post-trained
Training: Co-developed with All Hands AI.
The strongest Apache-licensed dense coder that fits on a single consumer GPU.
Quick start
ollama run devstral-small2:24bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Devstral Small 2 24B need?
At the recommended Q4_K_M quantization, Devstral Small 2 24B needs about 14 GB of VRAM. Q8_0 takes 26 GB, and unquantized FP16 weights take 48 GB.
Can Devstral Small 2 24B run without a GPU?
Yes — with roughly 24 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Devstral Small 2 24B support?
Devstral Small 2 24B supports a 250k-token context window (256,000 tokens).
Can I use Devstral Small 2 24B commercially?
Yes. Devstral Small 2 24B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Devstral Small 2 24B on consumer hardware?
Our compatibility engine estimates on the order of 15 tokens/sec on a mid-range GPU and up to 40 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Devstral Small 2 24B should I download first?
Start with Q4_K_M (14 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.