# Atlas with Mistral Small 24B (Ollama): The Single-GPU Commercial Pick for 2026 > Mistral Small 24B (Ollama) fits Atlas on a single 16GB card: 14GB of weights, a 32K token (32,768) context, and Free (self-hosted) pricing. Mistral Small 24B (Ollama) is Mistral's 24B general model at 14GB, sized deliberately for a single consumer GPU. Inside Atlas the pricing is Free (self-hosted) and the context window is 32K tokens (32,768). It is Apache-licensed and strong at instruction following, which makes it a safer commercial choice than Codestral even though Codestral is the code specialist. The one trap to know before you configure it: Ollama renders 32K on the 24b and latest tags, notably less than the 128K the older 22b tag reports, so check what you pulled before setting limit.context in atlas.json. ## Key takeaways - Mistral Small 24B (Ollama) is 14GB of weights, roughly 16GB to serve, a deliberate fit for a single 16GB card. - Context window is 32K tokens (32,768) and pricing is Free (self-hosted). - Apache 2.0 licensing, so unlike Codestral it can be deployed commercially with no additional negotiation. - Tag trap: Ollama renders 32K on the 24b and latest tags, notably less than the 128K the older 22b tag reports. Check what you pulled before setting limit.context. - It is a general model, so a same-size code specialist will write better patches. Choose it for reliable structured output and tool-call formatting. ## What is Mistral Small 24B (Ollama) best at inside Atlas? Mistral Small 24B (Ollama) is best at reliable structured output and tool-call formatting inside Atlas, which is what actually determines whether an agent loop stays on the rails. At 14GB of weights and roughly 16GB to serve, it is a deliberate fit for a single 16GB card. Developers shopping for a local Atlas model tend to optimize for benchmark scores on code generation, and then discover the loop breaks somewhere else entirely: a malformed tool call, a JSON object with a trailing comma, an argument the model decided to rename. Mistral Small 24B is tuned in the other direction. Instruction following and structured output are its strengths, and inside Atlas that translates directly into turns that complete. 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, so a model that emits well-formed calls means you spend your attention reviewing diffs rather than restarting turns. ## Why does the Ollama tag for Mistral Small report 32K instead of 128K? Ollama renders 32K on the mistral-small 24b and latest tags, notably less than the 128K the older 22b tag reports. Register mistral-small:24b in atlas.json with limit.context 32768, matching what the tag reports rather than assuming 128K, or Atlas will overflow the prompt. This is the single most common misconfiguration on Mistral Small 24B, because the internet is full of guides written against the 22b tag. If you set limit.context to 131072 on a tag that actually serves a 32K token (32,768) window, Atlas will happily pack retrieved code chunks, unified diffs, and tool results up to a limit the server cannot honor, and the failure mode is truncation and confused output rather than a clean error. Check what you pulled before setting limit.context. The correct configuration for mistral-small:24b is limit.context 32768 and limit.output 8192. Verify the resolved window before you trust it, because a wrong context limit is invisible until it silently corrupts a long turn. ## How much does it cost to run Atlas on Mistral Small 24B? Mistral Small 24B (Ollama) is Free (self-hosted). The pull is 14GB, roughly 16GB of VRAM or unified memory is needed to serve it, and there is no per-token bill. The Apache 2.0 license means commercial deployment needs no additional negotiation. The economic argument for Mistral Small 24B is licensing as much as price. Codestral, the code specialist from the same lab, carries terms that make commercial deployment a conversation. Mistral Small 24B is Apache 2.0 licensed, so unlike Codestral it can be deployed commercially with no additional negotiation, and the pricing is Free (self-hosted). For a consultancy or an internal platform team that wants to hand every developer a working Atlas agent without a procurement cycle or a per-seat token budget, that combination is the whole reason to choose this model over a better coder. Confirm roughly 16GB of VRAM or unified memory is free before serving, and the recurring cost of the model is exactly zero. ## When should you pick a different model than Mistral Small 24B? Pick a different model than Mistral Small 24B (Ollama) when patch quality is the bottleneck. Mistral Small 24B is a general model, so a same-size code specialist will write better patches. Its 32K token (32,768) window is also modest for a 14GB model in 2026. Mistral Small 24B trades peak coding ability for licensing freedom and instruction reliability, and that trade is not always right. If the constraint on your Atlas sessions is the quality of the generated diff rather than the reliability of the tool call, a code specialist at the same 14GB footprint is the better buy, and Codestral exists precisely for that. If the constraint is context, the 32K window is a hard stop and a 128K local model is the answer. A practical middle path: keep mistral-small:24b in the model slot for its structured output, and use Atlas subagents, which can run in the foreground or in parallel background sessions, to fan out work rather than stuffing more into a single 32K prompt. ## How do you set up a fully local Atlas on Mistral Small 24B? Pull it with ollama pull mistral-small:24b (14GB, aliased to :latest), register it in the atlas.json ollama provider with limit.context 32768, set model to ollama/mistral-small:24b, and enable the Ollama embedder on the codebase index so semantic search never issues an outbound request. The last step is the one that makes Mistral Small 24B a genuinely private setup rather than a partially private one. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, and if you skip that, your repository is embedded by a hosted API before the local model ever reads a line of it. With the Ollama embedder enabled, Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion entirely on your machine. Favorite mistral-small:24b in the Atlas /models dialog for fast switching, because Atlas lets you switch the active model and provider on the fly with favorites and recents, and you will want to compare it against a coder tag on your own repo. ## Setup 1. Pull it: ollama pull mistral-small:24b (14GB, aliased to :latest) 2. Register mistral-small:24b in the atlas.json ollama provider with limit.context 32768 and limit.output 8192, matching what the tag reports rather than assuming 128K 3. Confirm roughly 16GB of VRAM or unified memory is free before serving 4. Set model to ollama/mistral-small:24b and favorite it in the Atlas /models dialog for fast switching 5. Enable the Ollama embedder on the codebase index so semantic search over your repo never issues an outbound request ## FAQ ### does mistral small 24b have a 128k context window No. Ollama renders 32K on the mistral-small 24b and latest tags, notably less than the 128K the older 22b tag reports. Set limit.context 32768 in atlas.json for the 24b tag. ### how much VRAM does mistral small 24b need Roughly 16GB of VRAM or unified memory to serve, from 14GB of weights. Mistral Small 24B was sized deliberately for a single 16GB consumer GPU. ### can I use mistral small 24b commercially Yes. Mistral Small 24B is Apache 2.0 licensed, so unlike Codestral it can be deployed commercially with no additional negotiation, and it is Free (self-hosted). ### mistral small 24b vs codestral for coding Codestral is the code specialist and will write better patches, but Mistral Small 24B is the safer commercial choice because of its Apache 2.0 license. Mistral Small 24B is a general model tuned for instruction following. ### how do I add mistral small to atlas.json Register mistral-small:24b in the atlas.json ollama provider with limit.context 32768 and limit.output 8192, then set model to ollama/mistral-small:24b and favorite it in the Atlas /models dialog. ### what is the best local model for tool calling in an agent Mistral Small 24B is a strong candidate at 14GB because of its reliable structured output and tool-call formatting, which is what actually determines whether an agent loop stays on the rails. ### how do I keep my code private when using atlas locally Enable the Ollama embedder on the codebase index so semantic search over your repo never issues an outbound request, and serve Mistral Small 24B locally so no code reaches a third-party server. --- Canonical HTML: https://runatlas.sh/resources/models/mistral-small-24b-local Source of truth: aeo_pages row `/resources/models/mistral-small-24b-local` (segment: Models) (this file is generated from it, never hand-edited). Licence: Atlas is proprietary with a free core. It is not open source and there is no public source repository.