What is the context window for Gemma 3 12B (Ollama) in Atlas?

Gemma 3 12B (Ollama) provides a substantial 128K tokens (131,072) context window for Atlas, allowing it to process extensive codebases and project details for more informed AI assistance.

How much does Gemma 3 12B (Ollama) cost to use with Atlas?

Gemma 3 12B (Ollama) is Free (self-hosted) when used with Atlas, meaning there are no direct model costs. You only need to account for the local hardware resources required to run it.

Can Atlas use Gemma 3 12B (Ollama) to understand images?

Yes, Gemma 3 12B (Ollama) supports multimodal input, enabling Atlas to understand screenshots and diagrams directly within the prompt. This is particularly useful for debugging UI issues or interpreting visual documentation.

What are the hardware requirements for Gemma 3 12B (Ollama)?

Gemma 3 12B (Ollama) has 8.1GB weights and requires roughly 10GB of memory to serve. This makes it suitable for systems equipped with a 12GB GPU card for optimal performance.

Is Gemma 3 12B (Ollama) good for generating code diffs in Atlas?

While capable, Gemma 3 12B (Ollama) is a general-purpose model. For optimal code diff generation in Atlas, a 14B code specialist model might produce better, more precise results, even with a similar footprint.

How does Gemma 3 12B (Ollama) help with code privacy in Atlas?

By running Gemma 3 12B (Ollama) locally and utilizing the local Ollama embedder, Atlas can build its code index without sending any code to third-party servers, significantly enhancing code privacy and security.

Can I switch models in Atlas if I use Gemma 3 12B (Ollama)?

Yes, Atlas lets you switch the active model and provider on the fly. You can use Gemma 3 12B (Ollama) for the planning and review phases and then switch to a different, potentially more specialized, model for the build phase.

Atlas with Gemma 3 12B (Ollama) in 2026

Gemma 3 12B (Ollama) is excellent for Atlas users who need a large 128K context window and multimodal input for understanding screenshots and diagrams, all while being Free (self-hosted). It enables powerful local AI coding without third-party server reliance, making it a compelling choice for developers in 2026.

What is Gemma 3 12B (Ollama) best for with Atlas?

Gemma 3 12B (Ollama) excels within Atlas for developers in 2026 seeking a robust, locally hosted AI agent with a massive 128K context window. This model's multimodal capabilities allow Atlas to interpret screenshots and diagrams directly in the prompt, enhancing problem-solving.

Gemma 3 12B (Ollama) is best suited for Atlas users who prioritize a large context window and multimodal input for their AI coding agent. With its 128K token context, Atlas can process extensive codebases, leveraging its AST declaration indexing and Axis, the hybrid semantic and keyword code search, to provide highly relevant information to the model. The multimodal capability means Atlas can directly ingest screenshots of failing UIs or diagrams, feeding them into the prompt for more informed problem-solving. Furthermore, running Gemma 3 12B (Ollama) locally allows Atlas to build its code index with local Ollama embeddings, ensuring code remains off third-party servers and enhancing privacy for sensitive projects. This model is particularly effective in the planning and review stages of Atlas's read-only plan agent, where a broad understanding of the project context is crucial before switching to a build agent.

What are the cost and context window of Gemma 3 12B (Ollama)?

Gemma 3 12B (Ollama) provides a significant 128K token context window, a sixteen-fold increase over Gemma 2's 8K, at no direct cost to the developer in 2026. This model is Free (self-hosted), requiring only local hardware resources.

The primary advantage of Gemma 3 12B (Ollama) is its Free (self-hosted) pricing, eliminating ongoing API costs for developers using Atlas. This makes it an attractive option for budget-conscious teams or those requiring strict data sovereignty. The model boasts a substantial context window of 128K tokens (131,072), a significant leap from its predecessor. This large context window allows Atlas to maintain a comprehensive understanding of your codebase, git branches, status, and diffs, enabling more accurate and context-aware operations. While the model weights are 8.1GB, it requires roughly 10GB of memory to serve, which comfortably fits on a 12GB GPU card, making it accessible for many local development setups. This combination of a vast context window and no direct cost provides a powerful foundation for Atlas's AI coding capabilities.

What are the tradeoffs of using Gemma 3 12B (Ollama) with Atlas?

While Gemma 3 12B (Ollama) offers a compelling 128K context and multimodal input, developers in 2026 should note its general-purpose nature. A 14B code specialist model might produce superior diffs for Atlas's code editing tasks, despite a similar footprint.

Despite its strengths, Gemma 3 12B (Ollama) comes with specific tradeoffs when integrated with Atlas. As a general-purpose model, it may not always match the precision of a dedicated code specialist model, such as a 14B code specialist, when generating code diffs or performing highly specialized coding tasks. Developers might find that a code specialist model produces better, more idiomatic diffs for Atlas's unified diff approval flow. Another consideration is memory usage: the combination of vision capabilities and a long 128K context window means that memory consumption can inflate well past the 10GB weight floor, potentially straining systems with less than 12GB of VRAM, especially under heavy load. Atlas's ability to switch the active model and provider on the fly with favorites and recents can mitigate this tradeoff, allowing users to leverage Gemma 3 12B (Ollama) for planning and then switch to a more specialized coder tag for the build phase.

How does Gemma 3 12B (Ollama) enhance Atlas's capabilities?

Gemma 3 12B (Ollama) significantly enhances Atlas's capabilities in 2026 by providing a 128K token context window, allowing Atlas to process vast amounts of code and project information. Its multimodal input means Atlas can directly interpret UI screenshots, streamlining debugging workflows.

Gemma 3 12B (Ollama) brings several key enhancements to Atlas. The expansive 128K token context window allows Atlas to ingest and reason over significantly more project data, including detailed git branches, status, and diffs, leading to more informed decisions when staging and creating commits. This large context is crucial for Atlas's ability to index code by AST declarations using tree-sitter and to perform Axis, the hybrid semantic and keyword retrieval, effectively. The multimodal input is a material difference, enabling Atlas to understand visual information like screenshots of a failing UI or architectural diagrams, which can be directly fed into the prompt. This capability streamlines debugging and problem-solving workflows within Atlas. Furthermore, by supporting local Ollama embeddings, Gemma 3 12B (Ollama) allows Atlas to build its code index entirely on your machine, ensuring that sensitive code never leaves your local environment. This model fits direct into Atlas's plan-then-build flow, providing a robust foundation for the read-only plan agent before transitioning to the build agent for code modifications.

Setup

011: Pull the mid size: `ollama pull gemma3:12b` (8.1GB)
022: Add `gemma3:12b` to the ollama provider models map in `atlas.json` with `limit.context 131072` and `limit.output 8192`.
033: Serve Ollama locally and check the model resolves with `atlas models ollama`.
044: Use it as the planning and review model in the Atlas plan-then-build flow, switching to a coder tag with `/models` for the build phase.
055: Run the codebase index against the local Ollama embedder so a 128K window can be packed with your own code without any of it being uploaded.

Frequently asked questions

What is the context window for Gemma 3 12B (Ollama) in Atlas?: Gemma 3 12B (Ollama) provides a substantial 128K tokens (131,072) context window for Atlas, allowing it to process extensive codebases and project details for more informed AI assistance.
How much does Gemma 3 12B (Ollama) cost to use with Atlas?: Gemma 3 12B (Ollama) is Free (self-hosted) when used with Atlas, meaning there are no direct model costs. You only need to account for the local hardware resources required to run it.
Can Atlas use Gemma 3 12B (Ollama) to understand images?: Yes, Gemma 3 12B (Ollama) supports multimodal input, enabling Atlas to understand screenshots and diagrams directly within the prompt. This is particularly useful for debugging UI issues or interpreting visual documentation.
What are the hardware requirements for Gemma 3 12B (Ollama)?: Gemma 3 12B (Ollama) has 8.1GB weights and requires roughly 10GB of memory to serve. This makes it suitable for systems equipped with a 12GB GPU card for optimal performance.
Is Gemma 3 12B (Ollama) good for generating code diffs in Atlas?: While capable, Gemma 3 12B (Ollama) is a general-purpose model. For optimal code diff generation in Atlas, a 14B code specialist model might produce better, more precise results, even with a similar footprint.
How does Gemma 3 12B (Ollama) help with code privacy in Atlas?: By running Gemma 3 12B (Ollama) locally and utilizing the local Ollama embedder, Atlas can build its code index without sending any code to third-party servers, significantly enhancing code privacy and security.
Can I switch models in Atlas if I use Gemma 3 12B (Ollama)?: Yes, Atlas lets you switch the active model and provider on the fly. You can use Gemma 3 12B (Ollama) for the planning and review phases and then switch to a different, potentially more specialized, model for the build phase.

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