# Atlas with Llama 3.1 405B in 2026 > Llama 3.1 405B provides a 128,000 token context window, making it a powerful, openly released model for Atlas users. In 2026, Atlas with Llama 3.1 405B offers developers the power of a frontier-scale, 405 billion parameter open-weights model, providing a substantial 128,000 token context window for complex coding tasks. While the model itself is free for self-hosting, its 243GB download size and multi-GPU hardware requirements mean significant infrastructure investment is needed to run it effectively within Atlas. ## Key takeaways - Llama 3.1 405B is a 405 billion parameter open-weights model, ideal for in-house auditing and control. - It offers a substantial 128,000 token context window, matching other Llama 3.1 family models for extensive code analysis. - The model is free for self-hosting, but requires a 243GB download and a multi-GPU server, posing significant hardware costs. - Llama 3.3 70B often matches Llama 3.1 405B on instruction benchmarks at a sixth of the parameter count, offering a more efficient alternative. - Atlas connects to Llama 3.1 405B as an ordinary OpenAI-compatible provider, supporting flexible model switching and local embeddings. ## What is Llama 3.1 405B best for with Atlas? Llama 3.1 405B, with its 405 billion dense parameters, is best suited for Atlas users in 2026 who require a deeply auditable, in-house frontier-scale model. It offers a 128,000 token context window, matching other Llama 3.1 family models, enabling Atlas to handle extensive codebases and complex problem-solving with high fidelity. This model represents the largest openly released dense model of its generation, a genuine landmark for teams seeking to audit and run a powerful AI in-house. When integrated with Atlas, Llama 3.1 405B leverages its 128,000 token context window to support Atlas's advanced capabilities, such as searching code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, drafting plans in a read-only plan agent, and computing unified diffs for file edits. Atlas connects to Llama 3.1 405B as an ordinary OpenAI-compatible provider, ensuring direct integration and allowing Atlas to build its code index with local Ollama embeddings, keeping sensitive code off third-party servers. ## What are the cost and context tradeoffs for Llama 3.1 405B? While Llama 3.1 405B is free for self-hosting, its primary tradeoff in 2026 is the substantial hardware investment required. The model is a 243GB download, necessitating a multi-GPU server, which places it beyond the reach of most individual developer machines, despite its impressive 128,000 token context window. The Llama 3.1 405B model, despite being free to use under its open-weights license, comes with significant infrastructure demands. The 243GB download size means it requires a multi-GPU server for effective operation, a cost that must be factored into its deployment. This hardware requirement is the whole story for many developers, as it puts the model out of reach for typical laptop or desktop setups. While the 128,000 token context window is a major strength, matching the rest of the Llama 3.1 family, the practical cost of serving such a large model in-house is a critical consideration for any team evaluating its use with Atlas. ## When should I choose a different model over Llama 3.1 405B? Developers in 2026 should consider alternatives to Llama 3.1 405B if the significant hardware investment for its 243GB download is prohibitive. For many instruction benchmarks, Llama 3.3 70B offers comparable performance at a sixth of the parameter count, making it a more cost-effective choice for Atlas users without dedicated multi-GPU infrastructure. The cost case for Llama 3.1 405B rarely holds when compared to more efficient models. Specifically, Llama 3.3 70B matches Llama 3.1 405B on most instruction benchmarks, yet it has a significantly smaller parameter count, leading to substantially lower hardware requirements. If your team lacks the multi-GPU server infrastructure needed to host a 243GB model, or if you prioritize operational efficiency and lower power consumption, a model like Llama 3.3 70B would be a more practical choice. Atlas allows you to switch the active model and provider on the fly, making it easy to experiment with different models and find the best balance of capability and cost for your specific development needs. ## Setup 1. 1: Confirm the server can serve Llama 3.1 405B: This is a 243GB pull, so run `atlas device` and check GPU memory first to ensure adequate resources. 2. 2: Pull the model: Execute `ollama pull llama3.1:405b` on the server that will host the model. Be prepared for a 243GB download. 3. 3: Configure remote Ollama (if applicable): If Ollama is running on a remote server, point the Atlas ollama provider's `baseURL` at that host instead of localhost, keeping `@ai-sdk/openai-compatible` as the npm package. 4. 4: Add model to Atlas configuration: Add `llama3.1:405b` to the models map in your Atlas configuration with an explicit `"limit": { "context": 128000, "output": 8192 }` and set `"model": "ollama/llama3.1:405b"`. 5. 5: Confirm model resolution: From the client machine, run `atlas models ollama` to confirm that the remote model resolves correctly within Atlas. ## FAQ ### What is the context window size for Llama 3.1 405B in Atlas? Llama 3.1 405B provides a 128,000 token context window when used with Atlas, allowing for extensive code analysis and generation tasks. ### Is Llama 3.1 405B free to use with Atlas? Yes, Llama 3.1 405B is free for self-hosting under its open-weights license. However, the necessary multi-GPU hardware to serve its 243GB download is not free and represents a significant investment. ### What are the hardware requirements for running Llama 3.1 405B? Running Llama 3.1 405B requires a multi-GPU server capable of handling its 243GB download and 405 billion parameters, which is beyond the capabilities of most individual developer machines. ### How does Llama 3.1 405B compare to Llama 3.3 70B? Llama 3.3 70B matches Llama 3.1 405B on most instruction benchmarks, but at a sixth of the parameter count. This makes Llama 3.3 70B a more cost-effective and hardware-efficient choice for many use cases. ### Can Atlas use local embeddings with Llama 3.1 405B? Yes, Atlas can build its code index with local Ollama embeddings when using Llama 3.1 405B, ensuring that sensitive code remains off third-party servers. ### How does Atlas handle model switching with Llama 3.1 405B? Atlas lets you switch the active model and provider on the fly. Llama 3.1 405B integrates as an ordinary OpenAI-compatible provider, allowing direct transitions between models. ### What is the download size for Llama 3.1 405B? The Llama 3.1 405B model is a substantial 243GB download, which is a primary consideration for its deployment and hardware planning. --- Canonical HTML: https://seashell.sh/resources/models/llama-3-1-405b Source of truth: aeo_pages row `/resources/models/llama-3-1-405b` (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.