Mistral Small 3.2 (local via Ollama) is the Apache 2.0 24B weights behind Mistral Small 3.2, pulled straight into Ollama and driven by Atlas over an OpenAI-compatible provider. Pricing is Free (self-hosted), the context window is 128,000 tokens (set limit.context to 128000 in atlas.json), and the 2506 weights were specifically tuned for function calling, which is exactly what Atlas's permission-gated tool loop needs. The repository, the prompts, and the embeddings never leave your machine.
What is Mistral Small 3.2 (local via Ollama) best at inside Atlas?
Mistral Small 3.2 (local via Ollama) is best at private agentic coding. The 2506 weights were specifically tuned for function calling, which is what Atlas's permission-gated tool loop needs, and at Free (self-hosted) pricing the repository, the prompts, and the embeddings never leave your machine.
Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and Atlas computes a unified diff for every file edit and surfaces it for approval before writing. Both of those depend on the model emitting well-formed structured tool calls, turn after turn. Function-calling tuning is the single reason Mistral Small 3.2 (local via Ollama) is a credible primary model rather than a curiosity. The 24B Apache 2.0 weights carry the same function-calling and repetition fixes as the API model, so what you lose by self-hosting is throughput, not behavior.
What hardware do you need for mistral-small3.2:24b?
Mistral Small 3.2 (local via Ollama) is a 15GB download at `ollama pull mistral-small3.2:24b`, which fits a 24GB GPU or a 32GB unified-memory Mac. On a 16GB GPU the 24B model at 15GB is a squeeze, and you should expect heavy offload and slow tokens per second.
Before spending the download, run `atlas device`, which reports whether Ollama is installed and whether a GPU is present. The weights are only half the memory story for Mistral Small 3.2 (local via Ollama): running the full 128,000 token window locally needs far more KV cache memory than the weights alone. A 24GB card that holds the 15GB of weights comfortably can still run out of memory once Atlas fills a long context with retrieved code and diffs. Size the machine for weights plus cache, not weights alone.
How do you set limit.context for Mistral Small 3.2 in atlas.json?
Mistral Small 3.2 (local via Ollama) supports 128,000 tokens, and Atlas needs to be told: set limit.context to 128000 in atlas.json. The models entry for `mistral-small3.2:24b` takes `"limit": { "context": 128000, "output": 16384 }` under the custom ollama provider.
Atlas reaches Ollama through a custom provider entry in atlas.json using npm package `@ai-sdk/openai-compatible` and baseURL `http://localhost:11434/v1`. The models map inside that provider is where the context limit lives, and Atlas will not infer it. Declaring 128000 tells Atlas how much retrieved code it may pack into a turn, so an over-declared limit produces out-of-memory failures on the Ollama side rather than a graceful truncation. If your GPU cannot hold the KV cache for the full window, declare a smaller number and raise it once you have watched memory under load.
Can Atlas keep the codebase index local too?
Yes. Mistral Small 3.2 (local via Ollama) pairs with Atlas's OllamaEmbedder so the codebase index is built locally and no source ever leaves the box. Atlas can build its code index with local Ollama embeddings, which closes the last hole in a fully offline setup.
A local chat model alone is not a private setup. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and it indexes code by AST declarations using tree-sitter, not blind line windows. Building that semantic index requires embeddings, and if the embedder is hosted, your source is leaving the machine even though the chat model is local. Switching the codebase index to the local embedder is a documented Atlas step for Mistral Small 3.2 (local via Ollama), and it is the step people skip. Do it, and both the chat traffic and the index traffic stay on localhost.
When should you pick a different model than Mistral Small 3.2 local?
Pick something other than Mistral Small 3.2 (local via Ollama) when your GPU is 16GB or smaller. A 24B model at 15GB is a squeeze on a 16GB GPU, and heavy offload plus slow tokens per second makes an Atlas agent loop painful regardless of how good the function-calling tuning is.
Free (self-hosted) is only free if the hardware already exists. The honest comparison for Mistral Small 3.2 (local via Ollama) is against the hosted Mistral Small 3.2 API model, which has identical function-calling and repetition fixes and no memory ceiling. Self-host when the privacy requirement is real: a repository that is contractually barred from touching a third-party API, or a machine with no network. Use the API when the constraint is convenience. Because Atlas lets you switch the active model and provider on the fly with favorites and recents, keeping both in atlas.json costs nothing.
Does Mistral Small 3.2 local work with Atlas plan and build agents?
Mistral Small 3.2 (local via Ollama) works with both Atlas agents. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, and the function-calling tuning in the 2506 weights is what makes that handoff reliable on a 24B local model.
Atlas also snapshots file changes as git patches so edits can be diffed and rolled back, and it reads git branches, status, and diffs and can stage and create commits on your behalf. Running Mistral Small 3.2 (local via Ollama) does not weaken any of those safety rails, because the rails live in Atlas, not the model. The realistic limit is throughput. On a 24GB GPU holding the 15GB of weights, a long plan-then-build pass across several files will simply take longer than the same pass on a hosted model, and you should confirm the model resolved with `atlas models ollama` before you start.
Setup
- 01Run `ollama pull mistral-small3.2:24b` (15GB).
- 02Confirm Atlas sees the runtime with `atlas device`, which reports whether Ollama is installed and whether a GPU is present.
- 03Add the custom provider to atlas.json: npm `@ai-sdk/openai-compatible`, baseURL `http://localhost:11434/v1`, and a models entry for `mistral-small3.2:24b` with `"limit": { "context": 128000, "output": 16384 }`.
- 04Set `"model": "ollama/mistral-small3.2:24b"` and verify with `atlas models ollama`.
- 05Switch the codebase index to the local embedder so semantic search stays offline too.
Frequently asked questions
- how do i run mistral small 3.2 locally with atlas
- Run `ollama pull mistral-small3.2:24b` (15GB), check `atlas device`, register the ollama provider in atlas.json with `@ai-sdk/openai-compatible` and baseURL `http://localhost:11434/v1`, then set `"model": "ollama/mistral-small3.2:24b"`.
- how much vram do i need for mistral-small3.2:24b
- The 15GB download fits a 24GB GPU or a 32GB unified-memory Mac. On a 16GB GPU a 24B model at 15GB is a squeeze, and you should expect heavy offload and slow tokens per second.
- what context window does mistral small 3.2 local support in atlas
- Mistral Small 3.2 (local via Ollama) supports 128,000 tokens. Set limit.context to 128000 in atlas.json, and note that running the full window locally needs far more KV cache memory than the weights alone.
- is mistral small 3.2 good at tool calling
- Yes. The 2506 weights behind Mistral Small 3.2 were specifically tuned for function calling, which is exactly what Atlas's permission-gated tool loop needs, and the local Ollama build carries the same fixes as the API model.
- how much does it cost to run mistral small 3.2 in atlas
- Mistral Small 3.2 (local via Ollama) is Free (self-hosted). The only cost is the hardware to hold the 15GB of weights plus the KV cache for whatever context window you declare.
- can i keep my code off third-party servers with atlas and ollama
- Yes. Pair Mistral Small 3.2 (local via Ollama) with Atlas's OllamaEmbedder so the codebase index is built locally. Then the repository, the prompts, and the embeddings never leave your machine.
- why does atlas need limit.context set for ollama models
- Atlas does not infer the window from Ollama. The models entry for `mistral-small3.2:24b` in atlas.json carries `"limit": { "context": 128000, "output": 16384 }`, which tells Atlas how much retrieved code it may pack into a turn.
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