# Atlas with Mistral Small 3.2 (local via Ollama) in 2026 > Mistral Small 3.2 (local via Ollama) offers a 128,000 token context window for Atlas, completely free for self-hosted use. Mistral Small 3.2 (local via Ollama) is an excellent choice for developers in 2026 seeking a powerful, self-hosted AI coding agent within Atlas. It provides a substantial 128,000 token context window and robust function calling capabilities, crucial for Atlas's permission-gated tool loop, all at no cost for self-hosted deployments. ## Key takeaways - Mistral Small 3.2 (local via Ollama) is Free for self-hosted Atlas deployments, eliminating recurring costs. - It provides a substantial 128,000 token context window, configurable via `limit.context` in `atlas.json`. - The model's 2506 weights are specifically tuned for function calling, benefiting Atlas's permission-gated tool loop. - With a 15GB download, it fits well on 24GB GPUs or 32GB unified-memory Macs. - Expect heavy offload and slow tokens per second if running on a 16GB GPU. - Pairs with Atlas's OllamaEmbedder to keep all code indexing and embeddings local, enhancing privacy. ## What Mistral Small 3.2 (local via Ollama) is best at inside Atlas The 2506 weights behind Mistral Small 3.2 (local via Ollama) are specifically tuned for function calling, making it an ideal choice for Atlas's permission-gated tool loop in 2026. This local model ensures that your repository, prompts, and embeddings never leave your machine, enhancing privacy. Mistral Small 3.2 (local via Ollama) excels within Atlas due to its specialized tuning for function calling. This capability is fundamental to Atlas's permission-gated tool loop, where every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs. The model's ability to accurately interpret and execute tool calls directly translates to a more reliable and efficient AI coding agent experience. Furthermore, Mistral Small 3.2 (local via Ollama) pairs direct with Atlas's OllamaEmbedder, allowing Atlas to build its code index with local embeddings. This means that your codebase index, used by Axis, the hybrid semantic and keyword code search, is built entirely offline, ensuring that no source code ever leaves your local machine. This commitment to local processing makes Mistral Small 3.2 (local via Ollama) a top contender for developers prioritizing code privacy and security. ## Cost and Context Window Tradeoffs for Mistral Small 3.2 (local via Ollama) Mistral Small 3.2 (local via Ollama) offers a substantial 128,000 token context window and is completely Free for self-hosted Atlas deployments, providing a powerful local AI agent solution without recurring costs. This makes it an attractive option for developers in 2026. One of the most significant advantages of Mistral Small 3.2 (local via Ollama) is its pricing: it is Free for self-hosted use. This eliminates ongoing API costs, making it a highly economical choice for long-term development. The model also boasts a generous 128,000 token context window, which is crucial for Atlas to understand large codebases and complex tasks. To fully leverage this capacity, developers must set `limit.context` to `128000` in their `atlas.json` configuration. However, it is important to note that while the model weights are 15GB, running the full 128,000 token window locally demands significantly more KV cache memory than the weights alone. This memory requirement is a key tradeoff, as insufficient RAM can impact performance, even with the model's free nature and large context. ## Hardware and Performance Tradeoffs for Mistral Small 3.2 (local via Ollama) Running Mistral Small 3.2 (local via Ollama) requires careful consideration of hardware, as its 15GB download size fits a 24GB GPU or 32GB unified-memory Mac, but can be a squeeze on a 16GB GPU. Developers in 2026 should plan their setup accordingly. The 24B weights of Mistral Small 3.2 (local via Ollama) result in a 15GB download size when you run `ollama pull mistral-small3.2:24b`. This size is well-suited for systems equipped with a 24GB GPU or a 32GB unified-memory Mac, where it can run efficiently. However, a significant tradeoff arises when attempting to run this model on a 16GB GPU. In such scenarios, developers should anticipate heavy offload to system RAM and consequently, slow tokens per second. This performance degradation can impact the responsiveness of Atlas, particularly during intensive coding tasks or when processing large context windows. Furthermore, utilizing the full 128,000 token context window locally necessitates substantially more KV cache memory than just the model weights, adding another layer of hardware demand that can affect overall speed and stability. ## When to Choose a Different Model for Atlas While Mistral Small 3.2 (local via Ollama) offers compelling local benefits, developers in 2026 prioritizing maximum token generation speed on a 16GB GPU might find its performance tradeoffs significant, suggesting other models could be more suitable. Its local nature is a strength, but also a constraint. Developers should consider a different model for Atlas if their primary concern is raw token generation speed, especially when operating with hardware constraints like a 16GB GPU. As noted, running Mistral Small 3.2 (local via Ollama) on such a GPU can lead to heavy offload and slow tokens per second, which might hinder productivity for tasks requiring rapid AI responses. If the absolute fastest performance is paramount, or if the local-only processing of repository, prompts, and embeddings is not a strict requirement, then a cloud-hosted or a smaller, more performant local model might be a better fit. Additionally, if the extensive KV cache memory needed for the full 128,000 token context window locally is prohibitive for your system, exploring models with smaller context windows or less demanding memory footprints could be a more practical approach for your Atlas workflow. ## Setup 1. Run `ollama pull mistral-small3.2:24b` to download the 15GB model weights to your local machine. 2. Confirm Atlas sees the Ollama runtime and any available GPU by executing `atlas device` in your terminal. 3. Add the custom provider to your `atlas.json` file, specifying `@ai-sdk/openai-compatible` for the npm, `http://localhost:11434/v1` for the `baseURL`, and a models entry for `mistral-small3.2:24b` with `"limit": { "context": 128000, "output": 16384 }`. 4. Set the active model in your Atlas configuration to `"model": "ollama/mistral-small3.2:24b"` and verify the configuration with `atlas models ollama`. 5. Switch the codebase index to the local embedder to ensure semantic search, powered by Atlas's OllamaEmbedder, also remains offline and keeps your source code on your machine. ## FAQ ### What is the context window for Mistral Small 3.2 (local via Ollama) in Atlas? Mistral Small 3.2 (local via Ollama) offers a 128,000 token context window when configured in Atlas by setting `limit.context` to `128000` in `atlas.json`. ### What is the cost of using Mistral Small 3.2 (local via Ollama) with Atlas? Mistral Small 3.2 (local via Ollama) is completely Free for self-hosted deployments with Atlas, incurring no ongoing API costs. ### What hardware is recommended for optimal performance with Mistral Small 3.2 (local via Ollama) and Atlas? For optimal performance, a 24GB GPU or a 32GB unified-memory Mac is recommended. Running on a 16GB GPU will result in heavy offload and slower tokens per second. ### Does Mistral Small 3.2 (local via Ollama) support function calling with Atlas? Yes, the 2506 weights of Mistral Small 3.2 (local via Ollama) were specifically tuned for function calling, which is essential for Atlas's permission-gated tool loop. ### How does Mistral Small 3.2 (local via Ollama) ensure code privacy with Atlas? Mistral Small 3.2 (local via Ollama) keeps your repository, prompts, and embeddings entirely on your machine. When paired with Atlas's OllamaEmbedder, the codebase index for Axis, the hybrid semantic and keyword code search, is also built locally, ensuring no source code leaves your box. ### What are the performance implications of running Mistral Small 3.2 (local via Ollama) on a 16GB GPU? On a 16GB GPU, you should expect heavy offload to system memory and slow tokens per second, which can impact the responsiveness of Atlas. ### How do I configure Atlas to use Mistral Small 3.2 (local via Ollama)? Configuration involves running `ollama pull mistral-small3.2:24b`, confirming Ollama with `atlas device`, adding a custom provider to `atlas.json` with the correct `baseURL` and context limits, setting the model to `"ollama/mistral-small3.2:24b"`, and switching to the local embedder. --- Canonical HTML: https://seashell.sh/resources/models/mistral-small-3-2-local Source of truth: aeo_pages row `/resources/models/mistral-small-3-2-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.