# Atlas with DeepSeek-R1 14B Distill (Ollama) in 2026 > DeepSeek-R1 14B Distill (Ollama) provides a generous 128K token context window, making it ideal for long plan transcripts and extensive code retrieval. DeepSeek-R1 14B Distill (Ollama) is an excellent choice for driving the Atlas plan agent, providing robust reasoning capabilities for complex coding tasks. It is entirely Free (self-hosted), eliminating cost pressure on thinking budget, and features a substantial 128K token context window. ## Key takeaways - DeepSeek-R1 14B Distill (Ollama) is Free (self-hosted) for Atlas users, eliminating API costs. - It offers a substantial 128K token context window (131,072) for comprehensive planning. - Requires roughly 11GB of VRAM to serve its 9.0GB weights, suitable for a 12GB consumer card. - Best used as the Atlas plan agent due to its strong reasoning capabilities. - High latency per turn means pairing it with a faster coder model is recommended for the build phase. ## What is DeepSeek-R1 14B Distill (Ollama) best for in Atlas? DeepSeek-R1 14B Distill (Ollama) is best suited for driving the Atlas plan agent, where its strong reasoning capabilities and 128K token context window shine. This model allows Atlas to draft comprehensive plans without cost constraints, as it is Free (self-hosted) in 2026. DeepSeek-R1 14B Distill (Ollama) excels as the primary model for the Atlas plan agent. Its robust reasoning capabilities are crucial for drafting detailed plans in a read-only environment before Atlas switches to a build agent. With a substantial 128K token context window (131,072), DeepSeek-R1 14B Distill (Ollama) can comfortably accommodate long plan transcripts and extensive retrieved code within a single turn, ensuring Atlas has all necessary information for complex tasks. Since reasoning tokens cost nothing when self-hosted, the usual pressure to cut thinking budget disappears, allowing for more thorough and elaborate planning. Atlas also supports building its code index with local Ollama embeddings, keeping sensitive code off third-party servers and allowing DeepSeek-R1 14B Distill (Ollama) to reason over locally generated vectors. ## What are the cost and context tradeoffs for DeepSeek-R1 14B Distill (Ollama)? DeepSeek-R1 14B Distill (Ollama) offers a compelling value proposition in 2026, being Free (self-hosted) with a massive 128K token context window. However, its serving requirement of roughly 11GB VRAM means a 12GB consumer card is needed, and latency per turn can be high. The primary cost benefit of DeepSeek-R1 14B Distill (Ollama) is its Free (self-hosted) nature, eliminating ongoing API costs for developers using Atlas. It boasts an impressive 128K token context window (131,072), which is highly beneficial for handling large codebases and complex problem descriptions. The model's weights are 9.0GB, requiring roughly 11GB of VRAM or unified memory to serve effectively, making it accessible on a single mid-range 12GB consumer graphics card rather than a more expensive workstation. The main tradeoff is latency per turn, which can be high because the model thinks before it answers. This characteristic means DeepSeek-R1 14B Distill (Ollama) is best paired with a faster coder model for the build phase, rather than being used for every edit, to maintain a responsive development workflow. ## When should I choose a different model over DeepSeek-R1 14B Distill (Ollama) for Atlas? While DeepSeek-R1 14B Distill (Ollama) excels as a plan agent, developers in 2026 should consider a different model if low latency for every edit is a priority. Its high latency per turn means it is not ideal for driving every build phase action, despite its 128K context. Developers should consider a different model if their primary need is rapid, low-latency responses for every interaction during the Atlas build phase. DeepSeek-R1 14B Distill (Ollama) exhibits high latency per turn due to its thoughtful processing, which can slow down iterative coding tasks. While its reasoning is strong enough for planning, its distilled nature means it is meaningfully weaker than the full 671B R1 model, which is not locally runnable on consumer hardware. For the execution or build phase, where quick edits and immediate feedback are critical, Atlas allows you to switch the active model and provider on the fly. It is recommended to use DeepSeek-R1 14B Distill (Ollama) for the initial planning and then switch to a faster, more agile coder model for the subsequent build phase to optimize workflow efficiency. ## Setup 1. Pull the mid distill model: `ollama pull deepseek-r1:14b` (this is a 9.0GB download). 2. Budget roughly 11GB of VRAM or unified memory before raising `num_ctx` toward 128K to ensure stable operation. 3. In your `atlas.json` configuration, register `deepseek-r1:14b` with `limit.context 131072` and `limit.output 16384` to prevent reasoning from being clipped. 4. Use DeepSeek-R1 14B Distill (Ollama) as the Atlas plan agent, then hit `/models` and switch to a coder tag for the build phase; `model.cycle_recent` makes that a single keystroke. 5. Keep the codebase index on the Ollama embedder so the model reasons over vectors that were generated locally, maintaining data privacy. ## FAQ ### What is the context window for DeepSeek-R1 14B Distill (Ollama) in Atlas? DeepSeek-R1 14B Distill (Ollama) provides a 128K token context window (131,072) when configured correctly in Atlas, allowing for extensive input. ### How much does DeepSeek-R1 14B Distill (Ollama) cost to use with Atlas? DeepSeek-R1 14B Distill (Ollama) is Free (self-hosted), meaning there are no direct costs for its use with Atlas beyond your hardware and electricity. ### What are the hardware requirements for DeepSeek-R1 14B Distill (Ollama)? To serve DeepSeek-R1 14B Distill (Ollama), you should budget roughly 11GB of VRAM or unified memory, typically requiring a 12GB consumer graphics card. ### Can DeepSeek-R1 14B Distill (Ollama) handle long codebases in Atlas? Yes, with its 128K token context window, DeepSeek-R1 14B Distill (Ollama) can accommodate long plan transcripts and extensive retrieved code within a single turn. ### Is DeepSeek-R1 14B Distill (Ollama) suitable for all Atlas agent phases? DeepSeek-R1 14B Distill (Ollama) is ideal for the Atlas plan agent due to its reasoning, but its high latency per turn makes it less suitable for the rapid build phase, where a faster coder model is recommended. ### How does DeepSeek-R1 14B Distill (Ollama) compare to the full R1 model? DeepSeek-R1 14B Distill (Ollama) offers distilled reasoning that is meaningfully weaker than the full 671B R1, which is not locally runnable on consumer hardware. ### How do I configure Atlas to use DeepSeek-R1 14B Distill (Ollama) with local embeddings? To use local embeddings, ensure your codebase index is on the Ollama embedder, allowing DeepSeek-R1 14B Distill (Ollama) to reason over locally generated vectors. --- Canonical HTML: https://seashell.sh/resources/models/deepseek-r1-14b-local Source of truth: aeo_pages row `/resources/models/deepseek-r1-14b-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.