# Atlas with DeepSeek Coder 6.7B (Ollama) in 2026 > DeepSeek Coder 6.7B (Ollama) offers a 16K tokens (16,384) context window and is available for free as a self-hosted solution. DeepSeek Coder 6.7B (Ollama) is an excellent choice for driving Atlas in 2026 if you prioritize self-hosting and minimal hardware requirements. This model is free to use, runs efficiently on a 6GB GPU, and provides a 16K token context window, making it suitable for developers needing a lean, local AI coding agent for terminal-native workflows. ## Key takeaways - DeepSeek Coder 6.7B (Ollama) is free (self-hosted) for Atlas users in 2026. - It offers a 16K tokens (16,384) context window, which can be tight for complex agentic tasks. - The model requires a 3.8GB download and roughly 6GB of GPU memory to serve. - Trained code-first from scratch, it excels at fill-in-the-middle style code completion. - It predates the current generation; a 7B Qwen2.5-Coder typically offers better performance. - Atlas can use local Ollama embeddings with this model, keeping your code off third-party servers. ## Why choose DeepSeek Coder 6.7B (Ollama) for Atlas? DeepSeek Coder 6.7B (Ollama) is a strong contender for Atlas in 2026, especially for developers seeking a free, self-hosted solution that runs efficiently on modest hardware. This model requires only a 3.8GB download and can operate on a 6GB GPU, making it accessible even on older laptops. Atlas users in 2026 will find DeepSeek Coder 6.7B (Ollama) particularly appealing due to its low resource footprint and self-hosted nature. The model's 3.8GB download size and roughly 6GB serving requirement mean it can run on hardware typically found in older laptops, providing a powerful AI coding agent without needing cloud resources. DeepSeek Coder 6.7B was trained code-first from scratch, rather than being fine-tuned from a general model. This specialized training is evident in its performance on fill-in-the-middle style code completion tasks, making it a reliable choice for developers focused on code generation within Atlas. Furthermore, both base and instruct tags ship at the same size, offering flexibility to drive Atlas as either a raw completion engine or a chat model, depending on your workflow preferences. ## What are the tradeoffs of DeepSeek Coder 6.7B (Ollama) with Atlas? While DeepSeek Coder 6.7B (Ollama) offers significant advantages, its 16K tokens (16,384) context window can be a limitation for Atlas's agentic workflows in 2026. This context size is tight when Atlas injects diffs and tool output on every turn, potentially leading to context overflow. Developers evaluating DeepSeek Coder 6.7B (Ollama) for Atlas in 2026 should be aware of its inherent tradeoffs. The 16K tokens (16,384) context window, while substantial for many tasks, can become constrained within Atlas's dynamic environment. Atlas frequently injects diffs, tool outputs, and other contextual information into the model's prompt, which can quickly consume the available context. This means the model might struggle with very large codebases or complex, multi-step agentic tasks where extensive context is required. Additionally, DeepSeek Coder 6.7B predates the current generation of code models. While it remains useful, it may not match the current performance of newer models, which could impact the quality or speed of Atlas's responses in certain scenarios. ## When should I consider an alternative to DeepSeek Coder 6.7B (Ollama) for Atlas? Developers in 2026 should consider alternatives to DeepSeek Coder 6.7B (Ollama) if raw performance is paramount and slightly more resources are available. While DeepSeek Coder 6.7B is a capable model, it predates the current generation, and a 7B Qwen2.5-Coder will typically outperform it with a similar footprint. If your development environment in 2026 allows for slightly more demanding models or if you require the absolute best performance for complex coding tasks, you might consider an alternative to DeepSeek Coder 6.7B (Ollama). The model, originating from 2023, is dated next to more recent releases. For instance, a 7B Qwen2.5-Coder will usually beat DeepSeek Coder 6.7B in terms of capability, often with nearly the same hardware footprint. While DeepSeek Coder 6.7B is a reasonable fallback on thin hardware and starts fast, if your projects frequently involve intricate logic, extensive refactoring, or require the most advanced code understanding, investing in a newer, more capable model might yield better results and a smoother Atlas experience. Atlas lets you switch the active model and provider on the fly, making it easy to experiment with different options. ## How does DeepSeek Coder 6.7B (Ollama) enhance Atlas's agentic capabilities? DeepSeek Coder 6.7B (Ollama) integrates effectively with Atlas's agentic capabilities, particularly by enabling local code indexing and permission-gated tool calls. Atlas can build its code index using local Ollama embeddings, ensuring code remains off third-party servers, a key privacy feature in 2026. Atlas leverages DeepSeek Coder 6.7B (Ollama) to enhance its agentic capabilities in several practical ways. A significant advantage is the ability for Atlas to build its code index with local Ollama embeddings. This means your codebase's semantic information, crucial for Atlas's hybrid semantic and keyword retrieval, stays entirely on your local machine, keeping code off third-party servers and addressing privacy concerns in 2026. When using a 2023-era model like DeepSeek Coder 6.7B, Atlas's permission-gated tool calls become especially valuable. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and setting permissions to 'ask' rather than 'allow' is recommended for this model, as it is more likely to propose a suboptimal or bad tool call. This provides an essential safety layer, allowing developers to review and approve actions before Atlas executes them. ## Setup 1. Pull the mid size DeepSeek Coder model: `ollama pull deepseek-coder:6.7b` (3.8GB download). 2. In your `atlas.json` configuration, define the Ollama provider on `@ai-sdk/openai-compatible` with `baseURL http://localhost:11434/v1`. 3. Register `deepseek-coder:6.7b` within Atlas, setting `limit.context` to `16384` and `limit.output` to `4096`. Do not overstate the context window, as this model truly offers 16K tokens. 4. Set Atlas permissions to `ask` rather than `allow` for this model, as a 2023-era model is more likely to propose a bad tool call, requiring user approval. 5. Point the codebase index at the local Ollama embedder so retrieval quality does not depend on shipping code to a cloud embedding API, maintaining local privacy. ## FAQ ### What is the context window for DeepSeek Coder 6.7B (Ollama) in Atlas? DeepSeek Coder 6.7B (Ollama) provides a 16K tokens (16,384) context window when used with Atlas. This size is important to consider for agentic tasks that inject diffs and tool output. ### What are the hardware requirements for running DeepSeek Coder 6.7B (Ollama) with Atlas? To run DeepSeek Coder 6.7B (Ollama) with Atlas, you will need to download 3.8GB of data, and the model requires roughly 6GB of GPU memory to serve. This makes it suitable for older laptops or systems with modest hardware. ### Is DeepSeek Coder 6.7B (Ollama) free to use with Atlas? Yes, DeepSeek Coder 6.7B (Ollama) is free to use as a self-hosted model with Atlas. This eliminates cloud costs and keeps your AI coding agent entirely local. ### How does DeepSeek Coder 6.7B (Ollama) compare to newer models like Qwen2.5-Coder for Atlas? DeepSeek Coder 6.7B (Ollama) predates the current generation of code models. While still useful, a 7B Qwen2.5-Coder will generally outperform it at nearly the same hardware footprint, offering more advanced capabilities for Atlas. ### Can Atlas use local embeddings with DeepSeek Coder 6.7B (Ollama) for code indexing? Yes, Atlas can build its code index with local Ollama embeddings when using DeepSeek Coder 6.7B (Ollama). This ensures that your code remains off third-party servers, enhancing privacy and security. ### Why should I set Atlas permissions to 'ask' with DeepSeek Coder 6.7B (Ollama)? Setting Atlas permissions to 'ask' is recommended for DeepSeek Coder 6.7B (Ollama) because a 2023-era model is more likely to propose a suboptimal or bad tool call. This setting allows you to review and approve actions before Atlas executes them, providing a crucial safety layer. ### Is DeepSeek Coder 6.7B (Ollama) suitable for fill-in-the-middle code completion in Atlas? Yes, DeepSeek Coder 6.7B (Ollama) was trained code-first from scratch, which makes it particularly strong at fill-in-the-middle style code completion tasks within Atlas, providing useful and relevant suggestions. --- Canonical HTML: https://seashell.sh/resources/models/deepseek-coder-6-7b-local Source of truth: aeo_pages row `/resources/models/deepseek-coder-6-7b-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.