# Atlas with Llama 3.1 405B: The 243GB Landmark in 2026 > Llama 3.1 405B is a 243GB download with a 128,000 token window, Free (self-hosted), though the hardware to serve it is not. Llama 3.1 405B is the largest openly released dense model of its generation, and a genuine landmark: 405 billion parameters with a 128,000 token window, published under a license that permits self-hosting. Pricing is Free (self-hosted), though the hardware to serve it is not. `ollama pull llama3.1:405b` is a 243GB download that needs a multi-GPU server, which is the whole story. Atlas drives it as an ordinary OpenAI-compatible provider, the same atlas.json shape that covers a 4.9GB laptop model. ## Key takeaways - Llama 3.1 405B is 405 billion dense parameters with a 128,000 token window, openly released for self-hosting. - `ollama pull llama3.1:405b` is a 243GB download and needs a multi-GPU server, out of reach of nearly every developer machine. - Pricing is Free (self-hosted), though the hardware to serve it is not. - Llama 3.3 70B matches it on most instruction benchmarks at a sixth of the parameter count, so the cost case rarely holds. - Atlas serves it as an ordinary OpenAI-compatible provider, so the same atlas.json shape covers it and a 4.9GB laptop model. ## What is Llama 3.1 405B best at inside Atlas? Llama 3.1 405B is best at giving an organization a frontier-scale model it can audit and run in-house. 405 billion dense parameters were openly released under a license that permits self-hosting, and inside Atlas the model behaves as an ordinary OpenAI-compatible provider with a 128,000 token window. The auditability is the argument. For a regulated team, a model whose weights sit on their own servers is qualitatively different from an API, no matter how good the API's terms are. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, so a self-hosted Llama 3.1 405B plus a local embedder means nothing about the codebase, the prompts, or the diffs ever leaves the organization's hardware. What that costs is 243GB of weights and a multi-GPU server to hold them. ## How big is the llama3.1:405b download and what does it need? Llama 3.1 405B is a 243GB download. `ollama pull llama3.1:405b` needs a multi-GPU server, which puts Llama 3.1 405B out of reach of nearly every developer machine, so confirm the box can serve it at all: run `atlas device` and check GPU memory first. Two hundred forty-three gigabytes is the number that decides this page. No laptop, no single consumer GPU, and no ordinary workstation holds Llama 3.1 405B. The realistic deployment is a shared server, which means Ollama is usually remote rather than on localhost. Atlas handles that: point the ollama provider's baseURL at that host instead of localhost, keeping `@ai-sdk/openai-compatible` as the npm package. Run `atlas device` before the pull, because a 243GB download onto a machine that cannot serve it is an expensive way to learn the requirement. ## How do you point Atlas at a remote Ollama serving Llama 3.1 405B? Llama 3.1 405B usually lives on a server, not a laptop, so the Atlas ollama provider's baseURL points at that host instead of localhost. The npm package stays `@ai-sdk/openai-compatible`, and the models map gets `llama3.1:405b` with an explicit `"limit": { "context": 128000, "output": 8192 }`. The elegance of the arrangement is that nothing else in atlas.json changes. Llama 3.1 405B is served through Atlas as an ordinary OpenAI-compatible provider, so the same atlas.json shape covers Llama 3.1 405B and a 4.9GB laptop model. Set `"model": "ollama/llama3.1:405b"`, then run `atlas models ollama` from the client machine to confirm the remote model resolves before you start a session. A model that fails to resolve on a remote host looks identical to one that failed locally, so verify explicitly. ## Does Llama 3.1 405B justify its cost against Llama 3.3 70B? Rarely. Llama 3.3 70B matches Llama 3.1 405B on most instruction benchmarks at a sixth of the parameter count, so the cost case for a 243GB model seldom holds. Llama 3.1 405B remains free to self-host, though the hardware to serve it is not. The honest read on Llama 3.1 405B in 2026 is that its importance is historical and institutional rather than practical. Getting the same instruction quality from 70B means the 405B server is buying you very little per dollar of hardware. Where Llama 3.1 405B still wins is symbolic and strategic: it is the frontier-scale model teams can actually audit and run in-house, and for some organizations that property is worth the multi-GPU server on its own. Nobody should choose Llama 3.1 405B expecting better Atlas output than a well-served 70B. ## What is the context window of Llama 3.1 405B in Atlas? Llama 3.1 405B has a 128,000 token context window, matching the rest of the Llama 3.1 family. Ollama lists llama3.1:405b with a 128K window, and Atlas needs it declared explicitly: add `"limit": { "context": 128000, "output": 8192 }` to the models map. Unlike the smaller Llama 3.1 models, Llama 3.1 405B is usually running on a server with enough memory that the full 128,000 tokens is genuinely usable, rather than a nominal figure the KV cache defeats. That is a real advantage. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion and indexes code by AST declarations using tree-sitter, so a 128,000 token budget on capable hardware lets Atlas assemble a very complete picture of a large service before proposing a single diff. ## When should you pick a different model than Llama 3.1 405B? Pick a different model unless you already have the multi-GPU server. Llama 3.1 405B is a 243GB download, and Llama 3.3 70B matches it on most instruction benchmarks at a sixth of the parameter count, so the hardware bill rarely pays for itself in Atlas output quality. The decision tree is short. If the requirement is privacy on ordinary hardware, a smaller local model plus Atlas's local Ollama embeddings gets you there for a fraction of the memory. If the requirement is quality, a hosted coding model wins on both quality and setup effort. If the requirement is a frontier-scale open model you can audit in-house, Llama 3.1 405B is the model, and the 243GB is simply the price. Atlas lets you switch the active model and provider on the fly with favorites and recents, so this is never a one-way door. ## Setup 1. Confirm the box can serve it at all: this is a 243GB pull, so run `atlas device` and check GPU memory first. 2. Run `ollama pull llama3.1:405b` (243GB) on the server that will host it. 3. If Ollama is remote, point the Atlas ollama provider's baseURL at that host instead of localhost, keeping `@ai-sdk/openai-compatible` as the npm package. 4. Add `llama3.1:405b` to the models map with an explicit `"limit": { "context": 128000, "output": 8192 }` and set `"model": "ollama/llama3.1:405b"`. 5. Run `atlas models ollama` from the client machine to confirm the remote model resolves. ## FAQ ### how big is the llama 3.1 405b download `ollama pull llama3.1:405b` is a 243GB download and needs a multi-GPU server, which puts Llama 3.1 405B out of reach of nearly every developer machine. ### how much does llama 3.1 405b cost to run Llama 3.1 405B is Free (self-hosted), though the hardware to serve it is not. The 243GB of weights requires a multi-GPU server, and that is the real cost. ### what is the context window of llama 3.1 405b Llama 3.1 405B has a 128,000 token context window, matching the rest of the Llama 3.1 family. Declare it in atlas.json with `"limit": { "context": 128000, "output": 8192 }`. ### how do i point atlas at a remote ollama server Point the Atlas ollama provider's baseURL at that host instead of localhost, keeping `@ai-sdk/openai-compatible` as the npm package, then run `atlas models ollama` from the client machine to confirm the remote model resolves. ### is llama 3.1 405b worth it over llama 3.3 70b Rarely. Llama 3.3 70B matches Llama 3.1 405B on most instruction benchmarks at a sixth of the parameter count, so the cost case for a 243GB model seldom holds outside of an audit or in-house-frontier requirement. ### can i run llama 3.1 405b on a mac No. Llama 3.1 405B is a 243GB pull that needs a multi-GPU server. Run `atlas device` and check GPU memory before spending the download. ### why does llama 3.1 405b matter Llama 3.1 405B is the largest openly released dense model of its generation: 405 billion parameters with a 128,000 token window, published under a license that permits self-hosting, so teams can audit and run frontier scale in-house. --- Canonical HTML: https://runatlas.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: Atlas is proprietary with a free core. It is not open source and there is no public source repository.