# Atlas with Devstral Small 2 24B (local via Ollama) in 2026 > Devstral Small 2 24B (local via Ollama) provides a robust 128K token context window, making it a strong contender for local agent-driven development. Devstral Small 2 24B (local via Ollama) is an excellent choice for developers in 2026 seeking a powerful, agent-first model to drive Atlas for on-device coding tasks. It excels at real repository patch generation and complex agent loops, all while being Free (self-hosted) and running efficiently on a 16GB GPU. ## Key takeaways - Devstral Small 2 24B (local via Ollama) is an agent-first model, ideal for Atlas's multi-step workflows. - It runs efficiently on a 16GB GPU, with a 14GB download at Q4_K_M quantization. - The model is Free (self-hosted), offering a 128K token context window locally. - It scores near the top of sub-30B models on SWE-Bench Verified for real repository patches. - No dedicated reasoning mode means complex debugging might require a different model. - The local 128K context is half of the 262,144 tokens available from the hosted Devstral 2 API. ## Why choose Devstral Small 2 24B (local via Ollama) for Atlas? Devstral Small 2 24B (local via Ollama) is a top-tier local model for Atlas in 2026, specifically trained by Mistral as an agent-first solution. It reliably performs within complex agent loops, unlike many chat models, and scores near the top of the sub-30B pack on SWE-Bench Verified benchmarks. This model is uniquely suited for Atlas because it was designed from the ground up for agent harnesses, not adapted from a general chat model. This agent-first training means Devstral Small 2 24B (local via Ollama) maintains coherence and effectiveness through Atlas's multi-step workflows, such as drafting a plan in a read-only plan agent before switching to a build agent. Its strong performance on SWE-Bench Verified, which measures real repository patches, indicates its capability to generate practical, actionable code changes. Atlas can leverage this model for tasks like searching code with Axis, its hybrid semantic and keyword retrieval, or for generating unified diffs for file edits that Atlas surfaces for approval. The model's ability to hold up in a real agent loop ensures that Atlas's permission-gated tool calls and subagent fan-out capabilities are effectively utilized, providing a smooth and reliable coding experience. ## What are the hardware requirements for Devstral Small 2 24B (local via Ollama)? Devstral Small 2 24B (local via Ollama) is highly accessible for local development in 2026, requiring only a 16GB GPU for its 14GB download at the default Q4_K_M quantization. This makes it a more economical choice compared to larger models like Qwen3-Coder 30B, which typically demand higher VRAM. Running Devstral Small 2 24B (local via Ollama) locally with Atlas is efficient due to its optimized size. The model's 14GB download footprint means it fits comfortably on a 16GB GPU, a common specification for developer workstations in 2026. This allows developers to keep their code off third-party servers by building Atlas's code index with local Ollama embeddings, enhancing privacy and control. Atlas's terminal-native TUI, rendered with SolidJS through the OpenTUI renderer, ensures that the interface remains responsive even when driving a local model. The ability to switch the active model and provider on the fly with Atlas's favorites and recents features means you can easily experiment with Devstral Small 2 24B (local via Ollama) without complex reconfigurations. ## What are the context window and pricing tradeoffs for Devstral Small 2 24B (local via Ollama)? Devstral Small 2 24B (local via Ollama) offers a substantial 128K token context window, which is Free (self-hosted), making it an attractive option for cost-conscious developers in 2026. However, it is important to note that this local Ollama build provides half the context of the 262,144 tokens available from the hosted Devstral 2 API. The primary advantage of Devstral Small 2 24B (local via Ollama) is its Free (self-hosted) pricing, eliminating ongoing API costs for developers. The 128K token context window, as served by Ollama, is generous for many coding tasks, allowing Atlas to process large codebases, read git branches, status, and diffs, and manage complex file edits. Atlas can snapshot file changes as git patches, ensuring edits can be diffed and rolled back, all within this substantial context. However, a key tradeoff is that the local Ollama build's 128K token context is not equivalent to the 262,144 tokens offered by the hosted Devstral 2 API. For extremely long context tasks where every token matters, this difference could necessitate switching to the hosted version or another model. Developers should evaluate their specific project needs to determine if the 128K local context window is sufficient for their longest code files or most extensive refactoring operations. ## When should I consider a different model than Devstral Small 2 24B (local via Ollama)? While Devstral Small 2 24B (local via Ollama) excels in agentic tasks, it lacks a dedicated reasoning mode, meaning genuinely hard debugging or complex architectural problem-solving in 2026 might require a different model. For such scenarios, a reasoning-focused model like a local deepseek-r1 would be a more suitable choice. Devstral Small 2 24B (local via Ollama) is optimized for generating code and executing agentic workflows, but its design does not include a specific reasoning mode. This means that for tasks demanding deep analytical thought, complex logical deduction, or intricate debugging of non-obvious issues, its performance may not match models specifically engineered for reasoning. Atlas's ability to switch the active model and provider on the fly is particularly useful here; developers can start with Devstral Small 2 24B (local via Ollama) for most coding tasks and then direct switch to a model like deepseek-r1 when encountering a problem that requires more abstract reasoning. This flexibility allows Atlas users to leverage the strengths of multiple models, optimizing both cost and capability for different phases of development. Additionally, if a project absolutely requires a context window larger than the 128K tokens served by Ollama, even for local use, exploring other local models or the hosted Devstral 2 API might be necessary. ## Setup 1. Pull the Devstral model: `ollama pull devstral` (This resolves to `devstral:24b`, the 14GB agent model.) 2. Configure the Ollama provider in `atlas.json`: Add or extend the `ollama` provider block with `"baseURL": "http://localhost:11434/v1"`. 3. Register the Devstral model in `atlas.json`: In the `models` map, register `"devstral:24b"` with `"limit": { "context": 131072 }` to accurately reflect the 128K tokens served by Ollama. 4. Confirm the setup: Run `atlas models ollama` to verify the configuration. 5. Select the model in Atlas: Choose `devstral:24b` from the `/models` interface within Atlas. ## FAQ ### What is Devstral Small 2 24B (local via Ollama) best used for with Atlas? Devstral Small 2 24B (local via Ollama) is best used with Atlas for agentic coding tasks, such as generating code, refactoring, and executing multi-step plans. Its agent-first training ensures it performs reliably within Atlas's structured workflows, including code search with Axis and generating unified diffs for approval. ### How much does it cost to use Devstral Small 2 24B (local via Ollama) with Atlas? Devstral Small 2 24B (local via Ollama) is Free (self-hosted). You only need to provide the local hardware to run it, eliminating any per-token or subscription costs for its use with Atlas. ### What is the context window size for Devstral Small 2 24B (local via Ollama)? The Devstral Small 2 24B model, when served locally via Ollama, provides a 128K token context window. This allows Atlas to handle substantial codebases and extensive file changes effectively. ### Can Devstral Small 2 24B (local via Ollama) handle complex debugging tasks? Devstral Small 2 24B (local via Ollama) does not have a dedicated reasoning mode. For genuinely hard debugging or complex architectural problem-solving, Atlas users should consider switching to a model specifically designed for reasoning, such as a local deepseek-r1. ### What are the hardware requirements to run Devstral Small 2 24B (local via Ollama)? To run Devstral Small 2 24B (local via Ollama) efficiently, a 16GB GPU is recommended. The model's default Q4_K_M quantization results in a 14GB download size, fitting comfortably within this VRAM capacity. ### Is the local Devstral Small 2 24B context window the same as the hosted API? No, the local Ollama build of Devstral Small 2 24B serves a 128K token context window, which is half the 262,144 tokens exposed by the hosted Devstral 2 API. They are not drop-in equivalent for long context scenarios. ### How does Devstral Small 2 24B (local via Ollama) perform on coding benchmarks? Devstral Small 2 24B (local via Ollama) scores near the top of the sub-30B pack on SWE-Bench Verified, a benchmark that measures the model's ability to generate real repository patches rather than isolated puzzles. This indicates strong performance for practical coding tasks within Atlas. --- Canonical HTML: https://seashell.sh/resources/models/devstral-local Source of truth: aeo_pages row `/resources/models/devstral-local` (segment: Models) (this file is generated from it, never hand-edited). Licence: SeaShell is proprietary with a free core. It is not open source and there is no public source repository.