# Atlas with Command R 35B (Ollama) in 2026 > Command R 35B (Ollama) provides a robust 128K token context window, making it a powerful, free, self-hosted option for Atlas. Command R 35B (Ollama) is an excellent choice for driving Atlas's retrieval augmented generation and multi-step tool use workflows, offering a substantial 128K token context window at no cost, as it is entirely self-hosted. Developers in 2026 can leverage its RAG capabilities directly within Atlas, enhancing code understanding and agent performance. ## Key takeaways - Command R 35B (Ollama) is free and self-hosted, eliminating per-token costs for Atlas users in 2026. - It offers a substantial 128K token context window (131,072 tokens) for deep code understanding within Atlas. - The model is purpose-built for RAG and multi-step tool use, aligning perfectly with Atlas's agent turns. - Requires approximately 21GB of VRAM to serve its 19GB weights, fitting a 24GB GPU card. - Its weights carry a non-production research license, requiring review for commercial use. - Command R 35B (Ollama) is not a code specialist; consider pairing it with a dedicated coder model for diff generation. ## What is Command R 35B (Ollama) best for with Atlas? Command R 35B (Ollama) excels at retrieval augmented generation (RAG) and multi-step tool use within Atlas, directly aligning with how Atlas agents operate in 2026. Its 128K token context window allows it to process extensive codebases effectively, leveraging Atlas's Axis, the hybrid semantic and keyword retrieval. Command R 35B (Ollama) is purpose-built for retrieval augmented generation and multi-step tool use, which perfectly matches the architecture of an Atlas agent turn. Atlas feeds retrieved code chunks into every turn, and Command R 35B (Ollama)'s design around grounded citation means it tends to point at the retrieved chunk it used rather than inventing information. This synergy is further enhanced by Atlas's Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion, ensuring Command R 35B (Ollama) receives well-formed, relevant context. Atlas can also build its code index with local Ollama embeddings, keeping sensitive code off third-party servers and allowing Command R 35B (Ollama) to run against a RAG index that never leaves your disk. This model's ability to handle a 128K token context window (131,072 tokens) makes it highly effective for navigating large codebases and complex tasks within the terminal-native AI coding agent. Atlas's permission-gated tool calls and read-only plan agent further benefit from Command R 35B (Ollama)'s multi-step tool use capabilities. ## What are the cost and context tradeoffs for Command R 35B (Ollama)? Command R 35B (Ollama) offers a significant 128K token context window (131,072 tokens) at no monetary cost, as it is entirely free and self-hosted for developers in 2026. This model requires approximately 21GB of VRAM to serve its 19GB weights, fitting comfortably on a 24GB GPU card. The primary advantage of Command R 35B (Ollama) is its pricing: it is Free because it is self-hosted. This eliminates per-token costs, making it an attractive option for extensive use within Atlas. It boasts a substantial 128K token context window (131,072 tokens), allowing Atlas to provide a vast amount of relevant code and documentation for the model to reason over. However, this model's 19GB weights require roughly 21GB of VRAM to serve, meaning a 24GB GPU card is necessary for optimal performance. A key tradeoff to consider is Cohere's non-production research license for these weights. Developers must review the terms before deploying Command R 35B (Ollama) for commercial work, as this license may restrict certain uses. While the context window is generous, the local hardware requirement and license terms are important considerations for any developer in 2026. ## When should I choose a different model over Command R 35B (Ollama) for Atlas? While Command R 35B (Ollama) excels at RAG and tool use, developers in 2026 should consider alternative models if their primary need is highly specialized code generation or diff creation. This model is not a code specialist, despite its 128K context window. Command R 35B (Ollama) is not a code specialist. While it is excellent for retrieval-heavy reasoning and understanding context provided by Atlas's Axis, it may not be the optimal choice for tasks that demand highly specialized code generation or precise diff creation. For these specific coding tasks, developers might find better performance by pairing Command R 35B (Ollama) with a dedicated code-focused model, letting Command R handle the retrieval and reasoning, and the code specialist handle the actual code modifications. Atlas allows you to switch the active model and provider on the fly, making it easy to experiment with different configurations. If your commercial project requires a model with a production-ready license without the need to review research terms, or if you lack the 24GB GPU capacity to self-host the 19GB weights, then a different model or a cloud-hosted alternative might be more suitable. Atlas's ability to compute a unified diff for every file edit and surface it for approval means that a strong diff generator can significantly enhance the agent's output quality. ## Setup 1. Pull Command R 35B (Ollama): Pull Command R 35B (Ollama) using Ollama: `ollama pull command-r:35b`. This model has 19GB of weights and is aliased to `:latest`. 2. Review License Terms: Review Cohere's license terms for the Command R 35B weights before using it on any commercial work, as it carries a non-production research license. 3. Register in Atlas Configuration: Register `command-r:35b` within the `atlas.json` configuration file under the Ollama provider section. 4. Configure Context and Output Limits: Configure the context and output limits for the model in `atlas.json`: set `limit.context` to `131072` and `limit.output` to `8192`. 5. Set Active Model: Set the active model in Atlas to `ollama/command-r:35b` to begin using it for your agent turns. 6. Leverage Atlas Hybrid Search: Leverage Atlas's Axis, the hybrid semantic and keyword search fused with reciprocal rank fusion, to feed Command R 35B (Ollama) well-formed retrieval context. 7. Generate Local Embeddings: Generate the necessary code context using the local Ollama embedder, ensuring the RAG-native model operates against an index that remains on your local disk. ## FAQ ### Is Command R 35B (Ollama) free to use with Atlas? Yes, Command R 35B (Ollama) is free because it is self-hosted. There are no per-token costs when running it locally with Atlas in 2026, making it a cost-effective option. ### What is the context window size for Command R 35B (Ollama)? Command R 35B (Ollama) offers a large 128K token context window, which translates to 131,072 tokens. This allows Atlas to provide extensive code context for the model's reasoning. ### Does Command R 35B (Ollama) require a powerful GPU? Yes, Command R 35B (Ollama) has 19GB of weights and requires approximately 21GB of VRAM to serve. A 24GB GPU card is necessary for optimal performance when self-hosting. ### Can Command R 35B (Ollama) be used for commercial projects? Command R 35B (Ollama)'s weights carry a non-production research license from Cohere. You must review these license terms carefully before deploying it for any commercial work. ### How does Command R 35B (Ollama) integrate with Atlas's code search? Command R 35B (Ollama) integrates direct with Atlas's Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion, which feeds it well-formed context. Atlas can also use local Ollama embeddings to build its code index, keeping code off third-party servers. ### Is Command R 35B (Ollama) good at generating code? Command R 35B (Ollama) is not a code specialist. While it excels at retrieval-heavy reasoning and tool use, for highly specialized code generation or precise diff creation, you might consider pairing it with a dedicated code-focused model within Atlas. ### How do I switch to Command R 35B (Ollama) in Atlas? Atlas lets you switch the active model and provider on the fly. After pulling the model and configuring `atlas.json` with `limit.context 131072` and `limit.output 8192`, you can set the model to `ollama/command-r:35b`. --- Canonical HTML: https://seashell.sh/resources/models/command-r-35b-local Source of truth: aeo_pages row `/resources/models/command-r-35b-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.