# Atlas with Llama 4 Scout (Ollama): A 10M-Token Window on Local Hardware in 2026 > Llama 4 Scout (Ollama) publishes a 10M-token context (10,485,760) on the tag, the widest window in the library, at Free (self-hosted) pricing. Llama 4 Scout (Ollama) is Meta's 16-expert mixture of experts (16x17B) at 67GB, with a 10M tokens (10,485,760 as published on the tag) context. Inside Atlas the pricing is Free (self-hosted), and Llama 4 Scout is natively multimodal, taking text and image input, so design screenshots and failing-UI captures go straight into an Atlas prompt. The published 10M window is the widest in the Ollama library by a wide margin, but it is aspirational at a footprint of roughly 69GB to serve. Start limit.context well below 10M and raise it as memory permits. ## Key takeaways - Llama 4 Scout (Ollama) publishes a 10M tokens (10,485,760) context on its tag, the widest window in the library and an order of magnitude past the 1M Granite 4 hybrids. - A 16-expert mixture of experts (16x17B) with only a fraction active per token, so generation is faster than 67GB of weights suggests. - Roughly 69GB to serve just for the weights, which means a multi-GPU rig or a very large unified-memory Mac. The 10M window is aspirational at that footprint. - Natively multimodal (text and image), so design screenshots and failing-UI captures go straight into an Atlas prompt. - The 128x17B Maverick tag is 245GB and firmly out of consumer range, so Scout is the only Llama 4 most people can host. Pricing is Free (self-hosted). ## What is the largest context window you can run locally in 2026? Llama 4 Scout (Ollama) publishes a 10M tokens (10,485,760) context on its tag, which is the widest window in the Ollama library and an order of magnitude past the 1M Granite 4 hybrids. Pricing in Atlas is Free (self-hosted), and the pull is 67GB. Llama 4 Scout is the answer to the context-length question, with a caveat stated plainly in the same breath. The 10M figure is what the published tag renders, and the honest read is that the 10M window is aspirational at a footprint of roughly 69GB to serve just for the weights. Key and value cache for millions of tokens is charged on top of that. The practical Atlas configuration reflects this: register llama4:scout with limit.context set well below the published 10M to start (131072 is a sane opening value) and raise it as memory permits. That is not a workaround, it is the correct way to bring up a model whose ceiling is far above your hardware. ## What hardware does Llama 4 Scout (Ollama) need? Llama 4 Scout (Ollama) needs roughly 69GB of VRAM or unified memory for the weights alone, from a 67GB pull. That means a multi-GPU rig or a very large unified-memory Mac. Confirm the memory before you spend the bandwidth on 67GB. The 16-expert mixture of experts (16x17B) architecture means only a fraction of the experts are active per token, so generation is faster than 67GB of weights suggests. What it does not mean is that the memory bill is smaller: all of Llama 4 Scout must be resident. Roughly 69GB is the number to plan against. For context on what else Meta shipped, the 128x17B Maverick tag is 245GB and firmly out of consumer range, so Llama 4 Scout is the only Llama 4 most people can host. If you can serve it, serve Ollama on that machine and expose http://localhost:11434/v1, or the host address if Atlas runs elsewhere, which is a common pattern when the model lives on a rig and Atlas runs on your laptop. ## Can Atlas take screenshots as input with Llama 4 Scout? Yes. Llama 4 Scout (Ollama) is natively multimodal, taking text and image input, so design screenshots and failing-UI captures go straight into an Atlas prompt. Very few local models in 2026 combine image input with a published 10M-token window and Free (self-hosted) pricing. Image input changes the shape of the work you can hand a local agent. A failing UI screenshot, a design mock, a rendered chart that looks wrong: with Llama 4 Scout in the model slot, those are inputs rather than things you have to describe in prose. Atlas then does what it always does with the resulting plan. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and Atlas snapshots file changes as git patches so edits can be diffed and rolled back. The multimodal input widens the front of the loop without changing the safety at the back of it. ## When should you pick a different model than Llama 4 Scout? Pick a different model than Llama 4 Scout (Ollama) if you do not have roughly 69GB of VRAM or unified memory. Llama 4 Scout is a 67GB pull, and at that footprint the published 10M-token context is aspirational rather than something you will actually fill. Llama 4 Scout is a hardware decision before it is a model decision. If you are on a single consumer card, the question is closed: Llama 4 Scout will not load, and there is no quantization story in the Context here that changes that. If you have a multi-GPU rig or a very large unified-memory Mac, the next question is whether you need the window or the image input, because those are the two things Llama 4 Scout uniquely offers. If you need neither, a smaller model leaves memory free for a longer KV cache and a second model. Atlas lets you switch the active model and provider on the fly with favorites and recents, so keeping a smaller tag registered alongside llama4:scout costs nothing. ## How do you exploit a huge context window without sending your repo to a vendor? Index the repo with Ollama embeddings. With a window as large as Llama 4 Scout's published 10M tokens, local-first retrieval is the only way to exploit it without shipping your whole codebase to a vendor, and Atlas can build its code index with local Ollama embeddings. A very large window creates an obvious temptation: put more of the repository into the prompt. Done through a hosted provider, that means your codebase crosses a vendor boundary on every turn, at scale, priced per token. Llama 4 Scout served locally at Free (self-hosted) pricing removes both problems, but only if retrieval is local too. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and it indexes code by AST declarations using tree-sitter, not blind line windows, so what fills Llama 4 Scout's window is whole declarations rather than arbitrary slices. Set model to ollama/llama4:scout and verify with atlas models ollama before you scale limit.context up. ## Setup 1. Confirm you have roughly 69GB of VRAM or unified memory, then run: ollama pull llama4:scout (67GB, also tagged :16x17b and :latest) 2. Serve Ollama on that machine and expose http://localhost:11434/v1 (or the host address if Atlas runs elsewhere) 3. In atlas.json register llama4:scout with limit.context set well below the published 10M to start (131072 is a sane opening value) and raise it as memory permits 4. Set model to ollama/llama4:scout and verify with atlas models ollama 5. Index the repo with Ollama embeddings; with a window this large, local-first retrieval is the only way to exploit it without shipping your whole codebase to a vendor ## FAQ ### what local model has the biggest context window Llama 4 Scout, with a 10M tokens (10,485,760) context as published on the Ollama tag. That is the widest window in the library and an order of magnitude past the 1M Granite 4 hybrids. ### how much VRAM does llama 4 scout need Roughly 69GB of VRAM or unified memory to serve the 67GB of weights, which means a multi-GPU rig or a very large unified-memory Mac. ### can I really use 10 million tokens of context on llama 4 scout Not at a 69GB weight footprint. The 10M window is aspirational there. Register llama4:scout with limit.context set well below the published 10M to start, with 131072 as a sane opening value, and raise it as memory permits. ### does llama 4 scout accept images Yes. Llama 4 Scout is natively multimodal (text and image), so design screenshots and failing-UI captures go straight into an Atlas prompt. ### llama 4 scout vs maverick The 128x17B Maverick tag is 245GB and firmly out of consumer range, so Llama 4 Scout at 67GB is the only Llama 4 most people can host. ### why is llama 4 scout fast for 67GB Llama 4 Scout is a 16-expert mixture of experts (16x17B) with only a fraction active per token, so generation is faster than 67GB of weights suggests, even though all the weights stay resident. ### how do I point atlas at ollama running on another machine Serve Ollama on that machine and expose http://localhost:11434/v1, or the host address if Atlas runs elsewhere, then set model to ollama/llama4:scout and verify with atlas models ollama. --- Canonical HTML: https://runatlas.sh/resources/models/llama4-scout-local Source of truth: aeo_pages row `/resources/models/llama4-scout-local` (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.