# Atlas with Llama 3.2 3B (Ollama) in 2026 > Llama 3.2 3B (Ollama) offers a substantial 128K token context window at no cost, making it a compelling choice for local Atlas deployments. Llama 3.2 3B (Ollama) is an excellent choice for driving Atlas locally in 2026, especially for developers seeking a free, self-hosted solution that runs on CPU-only laptops. This 2.0GB model provides a substantial 128K token context window, making it ideal for small model tasks, local embeddings, and maintaining code privacy without incurring API costs. ## Key takeaways - Llama 3.2 3B (Ollama) is a Free (self-hosted) model, ideal for local Atlas deployments. - It features a substantial 128K tokens (131,072) context window, impressive for its 3B size. - The model is a 2.0GB download, requiring roughly 4GB to serve, making it suitable for CPU-only laptops. - Instruction following is unusually good for a 3B model, distilled from larger Llama 3.1 variants. - It is text-only and not recommended for complex multi-file refactors in Atlas's build slot. - Atlas can use its local Ollama embeddings for Axis, the hybrid semantic and keyword code search, keeping code off third-party servers. ## What is Llama 3.2 3B (Ollama) best for with Atlas? Llama 3.2 3B (Ollama) excels as a local, free model for Atlas in 2026, particularly for CPU-only setups. This 2.0GB model, requiring roughly 4GB to serve, provides a full 128K token context window, making it the realistic floor for an Atlas setup without a dedicated GPU. For developers using Atlas, Llama 3.2 3B (Ollama) is uniquely suited for scenarios where cost, privacy, and hardware limitations are primary concerns. Its 2.0GB download size and ability to run on a CPU-only laptop mean Atlas can leverage AI capabilities without a GPU or cloud dependency. The model's 128K token context window is unusually long for its 3B size, allowing Atlas to maintain a significant transcript of interactions and code. Distilled from larger Llama 3.1 models, its instruction following is remarkably good for a model of this scale. This makes it perfect for setting as Atlas's `small_model`, handling titles, summaries, and cheap subagent calls locally and for free. Furthermore, Atlas can build its code index with local Ollama embeddings, ensuring code remains off third-party servers and enabling full hybrid semantic and keyword search through Axis, the hybrid semantic and keyword code search, even on an air-gapped machine where no API key exists at all. ## What are the cost and context window tradeoffs for Llama 3.2 3B (Ollama)? Llama 3.2 3B (Ollama) provides a significant 128K tokens (131,072) context window at a cost of Free (self-hosted), making it an attractive option for local Atlas deployments in 2026. This 2.0GB model requires approximately 4GB of RAM to serve, fitting comfortably on many laptops with integrated graphics. The primary advantage of Llama 3.2 3B (Ollama) is its cost structure: it is Free (self-hosted). This eliminates ongoing API expenses, making it an economical choice for developers. Its context window of 128K tokens (131,072) is substantial, allowing Atlas to process and retain a large amount of information during coding sessions. However, this model's 3B parameter count presents a clear tradeoff. While it handles instruction following well for its size, it is not designed for complex, multi-file refactoring tasks. Atlas users should understand that while the context window is long, the model's reasoning capabilities at this scale mean it belongs in the `small_model` slot, not in the `build` slot for extensive code modifications. It is also a text-only model, lacking the vision capabilities found in other Llama 3.2 variants, which might be a limitation for tasks requiring image understanding. ## When should I choose a different model over Llama 3.2 3B (Ollama) for Atlas? While Llama 3.2 3B (Ollama) is excellent for many Atlas tasks, its 3B parameter count means it will not carry a multi-file refactor. Developers in 2026 should consider alternative models when tackling complex, large-scale code changes or tasks requiring vision capabilities. Developers should opt for a more powerful model when their Atlas workflow involves significant, multi-file refactoring or intricate architectural changes across a codebase. Llama 3.2 3B (Ollama) is explicitly noted as not being capable of carrying a multi-file refactor; it is best utilized in Atlas's `small_model` slot for tasks like generating summaries, drafting initial plans in the read-only plan agent, or handling subagent calls. For more demanding `build` slot operations that involve chaining unsupervised writes or making broad code modifications, a larger, more capable model is necessary. Additionally, Llama 3.2 3B (Ollama) is a text-only model. If your development tasks with Atlas require processing images, such as interpreting diagrams or screenshots, you would need to select a different model from the Llama 3.2 family that includes vision variants, which are not part of this specific tag family. ## Setup 1. Pull the model using Ollama: `ollama pull llama3.2:3b` (This is a 2.0GB download, aliased to `:latest`). 2. Register `llama3.2:3b` in your `atlas.json` Ollama models map. Configure `limit.context` to `131072` and `limit.output` to `8192`. 3. Set Atlas's `small_model` configuration to `ollama/llama3.2:3b`. This ensures Atlas titles, summaries, and cheap subagent calls stay local and free. 4. Keep Atlas permissions on `ask`. A 3B model like Llama 3.2 3B (Ollama) should not be chaining unsupervised writes, so user approval is crucial. 5. Run the codebase index with the Ollama embedder. This allows even a CPU-only machine to get full hybrid semantic and keyword search without needing a cloud account. ## FAQ ### What is the context window of Llama 3.2 3B (Ollama) with Atlas? Llama 3.2 3B (Ollama) provides a 128K tokens (131,072) context window when used with Atlas. This allows it to maintain a long transcript of interactions and code during your development sessions. ### What is the cost of using Llama 3.2 3B (Ollama) with Atlas? Llama 3.2 3B (Ollama) is Free (self-hosted) when used with Atlas. This means there are no API costs, and you only need to manage the local resources for running the model. ### Can Llama 3.2 3B (Ollama) handle multi-file refactors in Atlas? No, Llama 3.2 3B (Ollama) will not carry a multi-file refactor. Its 3B parameter count means it is best suited for Atlas's `small_model` slot, handling tasks like summaries and initial plans, rather than complex, broad code modifications. ### What are the hardware requirements for Llama 3.2 3B (Ollama) with Atlas? Llama 3.2 3B (Ollama) is a 2.0GB model that requires roughly 4GB of RAM to serve. This makes it viable on a CPU-only laptop, representing the realistic floor for an Atlas setup with no GPU at all. ### Does Llama 3.2 3B (Ollama) support vision capabilities in Atlas? No, Llama 3.2 3B (Ollama) is a text-only model. Unlike other Llama 3.2 vision variants, this specific tag family does not include vision capabilities for use with Atlas. ### How does Atlas use Llama 3.2 3B (Ollama) for code indexing? Atlas can build its code index using local Ollama embeddings with Llama 3.2 3B (Ollama). This keeps your code off third-party servers and enables full hybrid semantic and keyword search through Axis, the hybrid semantic and keyword code search, even on CPU-only machines. ### Why should I keep Atlas permissions on 'ask' with Llama 3.2 3B (Ollama)? It is recommended to keep Atlas permissions on `ask` when using Llama 3.2 3B (Ollama). Given its 3B size, this model should not be chaining unsupervised writes, ensuring you approve all significant actions before they are executed. --- Canonical HTML: https://seashell.sh/resources/models/llama3-2-3b-local Source of truth: aeo_pages row `/resources/models/llama3-2-3b-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.