What is IBM Granite Code 8B (Ollama) and how does it work with Atlas?

IBM Granite Code 8B (Ollama) is a 4.6GB enterprise code model from IBM, designed for local deployment via Ollama. When used with Atlas, it provides a powerful, self-hosted AI agent that leverages a 125K token context window to understand and interact with large codebases. Atlas uses this model for tasks like code search with Axis, the hybrid semantic and keyword code search, planning, and generating code edits, all while keeping your code on-premise.

What is the context window for IBM Granite Code 8B (Ollama) in Atlas?

IBM Granite Code 8B (Ollama) offers an impressive 125K token context window on its instruct tag. This allows Atlas to process significantly more code and context in a single interaction compared to many other 8B models, which typically cap at 8K or 16K tokens. This extended window is crucial for Atlas's ability to analyze complex projects and provide comprehensive solutions.

What are the costs associated with using IBM Granite Code 8B (Ollama) with Atlas?

IBM Granite Code 8B (Ollama) is Free (self-hosted). This means there are no direct API costs or subscription fees for using the model with Atlas. You only incur the operational costs of running the model on your local hardware, which typically requires roughly 7GB of RAM for serving the model weights.

What are the hardware requirements for running IBM Granite Code 8B (Ollama) with Atlas?

To run IBM Granite Code 8B (Ollama) with Atlas, you will need approximately 7GB of RAM to serve the model weights. This makes it feasible to run on an 8GB GPU card. However, to fully utilize the 125K token context window, you will need to incrementally grow Ollama's `num_ctx` setting, as the 7GB figure does not include the memory required for the full 125K KV cache.

Why should I choose IBM Granite Code 8B (Ollama) over other 8B models for Atlas?

You should choose IBM Granite Code 8B (Ollama) for Atlas if you prioritize a massive 125K token context window, Free (self-hosted) operation, and enterprise-grade licensing with clear provenance documentation. These features are particularly valuable for organizations needing to keep code off third-party servers and clear procurement hurdles, even if its raw coding ability trails some other 7B or 8B models.

What are the main tradeoffs of using IBM Granite Code 8B (Ollama) with Atlas?

The main tradeoffs for IBM Granite Code 8B (Ollama) are that its raw coding ability trails models like Qwen2.5-Coder 7B, meaning you are prioritizing context and provenance over benchmark wins. Additionally, to get the 125K context, you must specifically pull the `granite-code:8b-instruct` tag, as base variants offer much smaller context windows, typically 4K tokens.

How does Atlas use IBM Granite Code 8B (Ollama) for code indexing?

Atlas can build its code index using local Ollama embeddings when configured with IBM Granite Code 8B (Ollama). This ensures that the entire retrieval layer, which feeds code context to the agent, remains entirely on-premise. This capability, combined with the model's enterprise licensing, helps maintain data privacy and compliance for sensitive codebases.

Can Atlas switch between IBM Granite Code 8B (Ollama) and other models?

Yes, Atlas is designed to let you switch the active model and provider on the fly. You can easily configure favorites and recents to toggle between IBM Granite Code 8B (Ollama) and other models, allowing you to choose the best tool for a specific task or project, leveraging its strengths for context-heavy tasks and other models for raw coding performance.

Atlas with IBM Granite Code 8B (Ollama) in 2026

IBM Granite Code 8B (Ollama) is an excellent choice for Atlas users in 2026 who prioritize a massive 125K token context window and enterprise-grade licensing for their coding agent, all while keeping costs at Free (self-hosted). This model allows Atlas to process significantly larger codebases on-premise, ensuring data privacy and compliance.

What is IBM Granite Code 8B (Ollama) best for with Atlas?

IBM Granite Code 8B (Ollama) excels within Atlas for developers in 2026 who need a substantial 125K token context window and the assurance of enterprise licensing. This 4.6GB model, served locally, allows Atlas to perform deep code analysis and generate comprehensive plans without sending sensitive code to third-party servers.

When integrated with Atlas, IBM Granite Code 8B (Ollama) provides a powerful combination of local execution and extensive context. Atlas can leverage the 125K token context window to search code with Axis, the hybrid semantic and keyword code search, and build its code index using local Ollama embeddings. This keeps all code off third-party servers, a critical feature for enterprise environments. The model's enterprise licensing and provenance documentation also simplify procurement, making it a viable option where research releases might not be. Atlas's ability to draft a plan in a read-only plan agent and ask for approval before switching to a build agent, combined with the deep context of Granite Code 8B, ensures well-informed and permission-gated operations. Furthermore, Atlas computes a unified diff for every file edit and surfaces it for approval, enhancing control over changes proposed by the agent.

What are the cost and context tradeoffs for IBM Granite Code 8B (Ollama)?

IBM Granite Code 8B (Ollama) offers a compelling value proposition in 2026, being Free (self-hosted) while providing a 125K token context window, a significant advantage over models typically capped at 8K or 16K tokens. However, users must specifically pull the 'instruct' tag to access this extended context.

The primary benefit of IBM Granite Code 8B (Ollama) is its cost-effectiveness and context depth. Being Free (self-hosted), it eliminates ongoing API costs, making it an attractive option for long-term use within Atlas. The 125K token context window on the instruct tag is a standout feature, allowing Atlas to handle large files and complex projects with a single model call. This is particularly useful for Atlas's capabilities like reading git branches, status, and diffs, or staging and creating commits. However, it is crucial to note that the base variants of this model, such as the 8b-base tag, offer much smaller windows, typically 4K tokens. To fully utilize the 125K context, developers must ensure they pull the `granite-code:8b-instruct` tag. While the download size is 4.6GB, serving the model requires roughly 7GB of RAM, meaning it can run on an 8GB GPU card, but the 125K KV cache will consume additional memory, requiring incremental growth of Ollama's `num_ctx` setting.

When should I choose a different model over IBM Granite Code 8B (Ollama)?

While IBM Granite Code 8B (Ollama) provides an exceptional 125K token context window and enterprise provenance, its raw coding ability trails models like Qwen2.5-Coder 7B. Developers in 2026 prioritizing peak benchmark performance for code generation over context depth or self-hosting benefits might find other options more suitable for Atlas.

The core tradeoff with IBM Granite Code 8B (Ollama) is its performance in raw coding benchmarks. If your primary use case for Atlas involves generating highly optimized or complex code snippets where absolute coding ability is paramount, you might find that models like Qwen2.5-Coder 7B offer superior results. IBM Granite Code 8B (Ollama) is designed to provide extensive context and clear enterprise licensing, not necessarily to win every coding benchmark. Atlas allows you to switch the active model and provider on the fly with favorites and recents, so you can easily experiment. If your project demands the highest possible code quality and you are willing to compromise on context window size, self-hosting, or enterprise provenance, then exploring other models might be beneficial. This model is ideal when the ability to process a vast amount of code at once, coupled with the security and cost benefits of self-hosting, outweighs the need for bleeding-edge code generation performance.

Setup

01Pull the instruct tag specifically: `ollama pull granite-code:8b-instruct` (4.6GB).
02Register `granite-code:8b-instruct` in the `atlas.json` Ollama models map with `limit.context 125000` and `limit.output 8192`.
03Grow Ollama's `num_ctx` toward the full 125K token window incrementally; the 7GB figure covers weights, not a 125K KV cache.
04Set the active model to `ollama/granite-code:8b-instruct` and verify with `atlas models ollama`.
05Run the codebase index with Ollama embeddings so an enterprise-licensed model is fed by an entirely on-premise retrieval layer.

Frequently asked questions

What is IBM Granite Code 8B (Ollama) and how does it work with Atlas?: IBM Granite Code 8B (Ollama) is a 4.6GB enterprise code model from IBM, designed for local deployment via Ollama. When used with Atlas, it provides a powerful, self-hosted AI agent that leverages a 125K token context window to understand and interact with large codebases. Atlas uses this model for tasks like code search with Axis, the hybrid semantic and keyword code search, planning, and generating code edits, all while keeping your code on-premise.
What is the context window for IBM Granite Code 8B (Ollama) in Atlas?: IBM Granite Code 8B (Ollama) offers an impressive 125K token context window on its instruct tag. This allows Atlas to process significantly more code and context in a single interaction compared to many other 8B models, which typically cap at 8K or 16K tokens. This extended window is crucial for Atlas's ability to analyze complex projects and provide comprehensive solutions.
What are the costs associated with using IBM Granite Code 8B (Ollama) with Atlas?: IBM Granite Code 8B (Ollama) is Free (self-hosted). This means there are no direct API costs or subscription fees for using the model with Atlas. You only incur the operational costs of running the model on your local hardware, which typically requires roughly 7GB of RAM for serving the model weights.
What are the hardware requirements for running IBM Granite Code 8B (Ollama) with Atlas?: To run IBM Granite Code 8B (Ollama) with Atlas, you will need approximately 7GB of RAM to serve the model weights. This makes it feasible to run on an 8GB GPU card. However, to fully utilize the 125K token context window, you will need to incrementally grow Ollama's `num_ctx` setting, as the 7GB figure does not include the memory required for the full 125K KV cache.
Why should I choose IBM Granite Code 8B (Ollama) over other 8B models for Atlas?: You should choose IBM Granite Code 8B (Ollama) for Atlas if you prioritize a massive 125K token context window, Free (self-hosted) operation, and enterprise-grade licensing with clear provenance documentation. These features are particularly valuable for organizations needing to keep code off third-party servers and clear procurement hurdles, even if its raw coding ability trails some other 7B or 8B models.
What are the main tradeoffs of using IBM Granite Code 8B (Ollama) with Atlas?: The main tradeoffs for IBM Granite Code 8B (Ollama) are that its raw coding ability trails models like Qwen2.5-Coder 7B, meaning you are prioritizing context and provenance over benchmark wins. Additionally, to get the 125K context, you must specifically pull the `granite-code:8b-instruct` tag, as base variants offer much smaller context windows, typically 4K tokens.
How does Atlas use IBM Granite Code 8B (Ollama) for code indexing?: Atlas can build its code index using local Ollama embeddings when configured with IBM Granite Code 8B (Ollama). This ensures that the entire retrieval layer, which feeds code context to the agent, remains entirely on-premise. This capability, combined with the model's enterprise licensing, helps maintain data privacy and compliance for sensitive codebases.
Can Atlas switch between IBM Granite Code 8B (Ollama) and other models?: Yes, Atlas is designed to let you switch the active model and provider on the fly. You can easily configure favorites and recents to toggle between IBM Granite Code 8B (Ollama) and other models, allowing you to choose the best tool for a specific task or project, leveraging its strengths for context-heavy tasks and other models for raw coding performance.

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