Devstral Small 2 24B (local via Ollama) is Mistral's agent-first local model, roughly 14GB quantized, with a 128K token context and no per-token cost. Inside Atlas, Devstral is the model to pick when your hardware tops out at a 16GB GPU but you still need something that holds up in a real agent loop rather than degrading into autocomplete. Its honest limits are no reasoning mode and a smaller window than the hosted Devstral 2 API.
What does agent-first training mean for Devstral Small 2 24B?
Devstral Small 2 24B was trained agent-first by Mistral for harnesses like Atlas, Cline, Aider, and OpenHands, not adapted from a chat model. That distinction is the reason Devstral is one of the few local models that reliably holds up inside a real agent loop.
Most local models are chat models with a coding dataset stirred in, and they reveal it the moment an agent asks them to take twenty tool-calling turns in a row. Devstral Small 2 24B was built for that shape of work from the start. In Atlas, that shows up where the loop is tightest. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, and Devstral holds the thread across that handoff instead of drifting back into explanatory prose. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, so a model that emits coherent calls saves you approvals.
Can Devstral Small 2 24B run on a 16GB GPU?
Yes. Devstral Small 2 24B is a 14GB download at the default Q4_K_M quant, so it runs on a 16GB GPU, a tier below what Qwen3-Coder 30B needs. That single fact is why Devstral is the practical Atlas choice on mainstream consumer hardware in 2026.
Hardware is the real gatekeeper for local agentic coding. Qwen3-Coder 30B wants a 24GB card. Devstral Small 2 24B fits the far more common 16GB tier at 14GB quantized, which puts a genuine agent-capable model on hardware most developers already own. Pull it with `ollama pull devstral`, the 24B agent model, 14GB, which resolves to devstral:24b. Then confirm with `atlas models ollama` before you start a session, so you find configuration problems at the command line rather than three turns into a refactor.
How does Devstral Small 2 24B score on SWE-Bench Verified?
Devstral Small 2 24B scores near the top of the sub-30B pack on SWE-Bench Verified, the benchmark that measures real repository patches rather than isolated puzzles. For an Atlas user, SWE-Bench Verified is the benchmark that actually predicts behavior in a working repo.
Benchmark choice matters as much as benchmark score. A model that aces isolated function-completion puzzles can still fail at applying a patch across a real repository, which is what Atlas asks of it. SWE-Bench Verified measures the latter, and Devstral Small 2 24B places near the top of the sub-30B pack on it. That maps directly onto the Atlas workflow, where Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and Atlas snapshots file changes as git patches so edits can be diffed and rolled back.
What is the context window of Devstral Small 2 24B on Ollama?
The Ollama build of Devstral Small 2 24B serves a 128K token window, which is half the 262,144 the hosted Devstral 2 API exposes. That gap means local and cloud Devstral are not drop-in equivalent on long context, and an Atlas workflow tuned for one may break on the other.
Register Devstral Small 2 24B in the models map with "limit": { "context": 131072 } to match what Ollama actually serves. Setting a larger number does not create capacity, it just moves the failure from Atlas's truncation logic into the model's. Because Atlas indexes code by AST declarations using tree-sitter, not blind line windows, a 128K budget still goes a long way, but a sprawling multi-file retrieval will hit the ceiling. Plan around 128K locally and do not assume the hosted 262,144 figure applies to your Ollama setup.
When should you pick a different model than Devstral Small 2 24B?
Pick a different model than Devstral Small 2 24B for genuinely hard debugging. Devstral Small 2 24B has no reasoning mode, so a stubborn bug still needs a reasoning model such as a local deepseek-r1, even though Devstral is the better agent-loop model of the two.
The clean split for Devstral Small 2 24B inside Atlas is diagnosis versus execution. When the failure is understood and the work is applying a patch across files, Devstral is the right seat: agent-first training, 14GB, running on a 16GB GPU. When nobody understands why the test fails, swap to a reasoning model. Atlas lets you switch the active model and provider on the fly with favorites and recents, so the swap is a keystroke, and you can return to Devstral for the implementation pass once the cause is clear.
Setup
- 01Run `ollama pull devstral` (the 24B agent model, 14GB, resolves to devstral:24b).
- 02Add or extend the ollama provider block in atlas.json with baseURL http://localhost:11434/v1.
- 03Register "devstral:24b" in the models map with "limit": { "context": 131072 } to match what Ollama actually serves.
- 04Run `atlas models ollama` to confirm Atlas resolved the model.
- 05Select Devstral Small 2 24B from /models in the Atlas TUI and start a build session.
Frequently asked questions
- what local model runs on a 16gb gpu for coding agents
- Devstral Small 2 24B. It is a 14GB download at the default Q4_K_M quant, so it runs on a 16GB GPU, a tier below what Qwen3-Coder 30B needs, and it was trained agent-first by Mistral.
- how do i install devstral for atlas
- Run `ollama pull devstral` (the 24B agent model, 14GB, resolves to devstral:24b), add the ollama provider block to atlas.json with baseURL http://localhost:11434/v1, register "devstral:24b" with "limit": { "context": 131072 }, then run `atlas models ollama` to confirm.
- what is the context window of devstral on ollama
- The Ollama build of Devstral Small 2 24B serves a 128K token window. The hosted Devstral 2 API exposes 262,144, so local and cloud are not drop-in equivalent on long context.
- devstral vs qwen3-coder 30b for local coding
- Devstral Small 2 24B is 14GB and runs on a 16GB GPU, and it was trained agent-first for harnesses like Atlas, Cline, Aider, and OpenHands. Qwen3-Coder 30B needs more hardware but offers a 256K window. Pick Devstral when your card is 16GB.
- does devstral have a reasoning mode
- No. Devstral Small 2 24B has no reasoning mode, so genuinely hard debugging still needs a reasoning model such as a local deepseek-r1. Use Devstral for the agent loop and the implementation pass.
- how much does devstral small 2 24b cost
- Devstral Small 2 24B is free, self-hosted. You run it locally through Ollama at http://localhost:11434/v1, so there is no per-token cost, only the 14GB download and your own hardware.
- is devstral good on swe-bench verified
- Devstral Small 2 24B scores near the top of the sub-30B pack on SWE-Bench Verified, which measures real repository patches rather than isolated puzzles. That maps closely onto how Atlas applies unified diffs across a repo.
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