# Atlas with Qwen2.5-Coder 7B (local via Ollama): the Laptop Setup in 2026 > Qwen2.5-Coder 7B runs comfortably in about 5GB at 4-bit quantization, so Atlas works on a laptop with no discrete GPU. Qwen2.5-Coder 7B (local via Ollama) is the small, dependable local coder. At 7B with a 32K token window it runs on almost any modern laptop, in about 5GB at 4-bit quantization, and it is the model the reference Atlas local configuration ships with. It costs nothing to self-host. The tradeoff is the 32,768 token context, an order of magnitude below Qwen3-Coder's 256K, which large files and long sessions overflow quickly. ## Key takeaways - Qwen2.5-Coder 7B runs comfortably in about 5GB at 4-bit quantization, so it works on a laptop with no discrete GPU. - It is the exact model Atlas's reference local configuration pins for both `model` and `small_model`. - The context window is 32,768 tokens, an order of magnitude below Qwen3-Coder's 256K. - Available at 0.5B, 1.5B, 3B, 7B, 14B, and 32B, giving a clean upgrade path as hardware improves. - It is a previous-generation model: qwen3-coder:30b beats it decisively wherever the hardware allows. ## Can you run Atlas on a laptop with no discrete GPU? Yes, with Qwen2.5-Coder 7B. Qwen2.5-Coder 7B runs comfortably in about 5GB at 4-bit quantization, so it works on a laptop with no discrete GPU. That 5GB footprint is the lowest bar of any model Atlas treats as a serious local default in 2026. Most local coding models assume a GPU. Qwen2.5-Coder 7B does not, and that is its entire reason to exist in the Atlas lineup. Pull it with `ollama pull qwen2.5-coder:7b` and you have a working agent on a machine that cannot host anything larger. Atlas behavior is unchanged on this hardware: Atlas indexes code by AST declarations using tree-sitter, not blind line windows, and every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs. The model is smaller, the harness is not. ## Why is Qwen2.5-Coder 7B the reference local configuration for Atlas? Qwen2.5-Coder 7B is the exact model that Atlas's reference local configuration pins for both `model` and `small_model`. Pinning one 7B model to both slots means a single 5GB download gets you a fully working local Atlas, with no second model to pull or configure. Reference configurations are chosen for the widest hardware, not the best score. Setting both slots to Qwen2.5-Coder 7B, with "model": "ollama/qwen2.5-coder:7b" and "small_model": "ollama/qwen2.5-coder:7b" in atlas.json, means the background jobs and the build agent share one loaded model, which matters enormously on a machine with limited memory. Loading two separate models on a laptop would evict one from memory constantly. One model in both slots avoids that entirely. ## Is a 32K context window enough for Atlas? A 32,768 token context window is tight for Atlas. Qwen2.5-Coder 7B provides exactly 32K, an order of magnitude below Qwen3-Coder's 256K, so large files and long sessions overflow quickly and you will feel it within a few dozen turns. Plan your Qwen2.5-Coder 7B sessions around the 32,768 token limit rather than fighting it. Keep tasks narrow: one file, one function, one bug. Register the honest limit in atlas.json with "limit": { "context": 32768, "output": 8192 } so Atlas trims deliberately instead of the model failing silently. Because Atlas snapshots file changes as git patches so edits can be diffed and rolled back, a session that goes off the rails from context overflow is cheap to undo, but it is cheaper still to keep the scope small in the first place. ## What is the upgrade path from Qwen2.5-Coder 7B? Qwen2.5-Coder is available at 0.5B, 1.5B, 3B, 7B, 14B, and 32B, giving a clean upgrade path as hardware improves. Moving from Qwen2.5-Coder 7B to the 14B or 32B variant is a one-line change in atlas.json, with no prompt rewriting required. The six-size ladder is the practical strength of the Qwen2.5-Coder family. A developer on a 2020 laptop can run the 7B or drop to 3B, and the same developer on new hardware next year can pull the 32B without changing how they work with Atlas. Run `atlas models ollama` after any size change to confirm what Atlas resolved. When the hardware allows something better than the family, however, the honest recommendation is to leave it: qwen3-coder:30b beats Qwen2.5-Coder 7B decisively. ## When should you pick a different model than Qwen2.5-Coder 7B? Pick a different model than Qwen2.5-Coder 7B as soon as the hardware allows. Qwen2.5-Coder 7B is a previous-generation model, and qwen3-coder:30b beats it decisively wherever the hardware allows, with a 256K window against Qwen2.5-Coder 7B's 32,768 tokens. Qwen2.5-Coder 7B earns its place on constrained machines and nowhere else. If you have a 24GB GPU or a 32GB Apple Silicon machine, Qwen3-Coder 30B is the better default by a wide margin, and the context difference alone, 256K against 32K, changes what Atlas can attempt. Atlas lets you switch the active model and provider on the fly with favorites and recents, so keep Qwen2.5-Coder 7B configured as the fallback for travel or a thin client, and run something larger when you are at your real workstation. ## Setup 1. Run `ollama pull qwen2.5-coder:7b` (about 5GB at 4-bit quantization). 2. Add the provider block to atlas.json: "ollama": { "npm": "@ai-sdk/openai-compatible", "name": "Ollama (local)", "options": { "baseURL": "http://localhost:11434/v1" }, "models": { "qwen2.5-coder:7b": { "name": "Qwen2.5 Coder 7B (local)", "limit": { "context": 32768, "output": 8192 } } } } 3. Set both slots: "model": "ollama/qwen2.5-coder:7b" and "small_model": "ollama/qwen2.5-coder:7b". 4. Run `atlas models ollama` to confirm, then start the TUI. 5. Keep tasks narrow, since the 32,768 token window overflows quickly on large files. ## FAQ ### can i run atlas on a laptop without a gpu Yes, with Qwen2.5-Coder 7B. It runs comfortably in about 5GB at 4-bit quantization, so it works on a laptop with no discrete GPU. Pull it with `ollama pull qwen2.5-coder:7b`. ### what is the context window of qwen2.5-coder 7b Qwen2.5-Coder 7B has a 32,768 token context window, an order of magnitude below Qwen3-Coder's 256K. Large files and long Atlas sessions overflow it quickly, so keep tasks narrow. ### what is the default local model in atlas reference config Qwen2.5-Coder 7B is the exact model Atlas's reference local configuration pins for both `model` and `small_model`, so a single 5GB download gets you a fully working local Atlas. ### what sizes does qwen2.5-coder come in Qwen2.5-Coder is available at 0.5B, 1.5B, 3B, 7B, 14B, and 32B, giving a clean upgrade path as hardware improves. The 7B is the one that fits a laptop with no discrete GPU. ### qwen2.5-coder 7b vs qwen3-coder 30b Qwen2.5-Coder 7B is a previous-generation model, and qwen3-coder:30b beats it decisively wherever the hardware allows. The context gap is stark: 32,768 tokens against 256K. ### how do i configure qwen2.5-coder 7b in atlas.json Add the ollama provider with baseURL http://localhost:11434/v1, register "qwen2.5-coder:7b" with "limit": { "context": 32768, "output": 8192 }, then set both "model": "ollama/qwen2.5-coder:7b" and "small_model": "ollama/qwen2.5-coder:7b". ### how much does qwen2.5-coder 7b cost Qwen2.5-Coder 7B is free, self-hosted through Ollama. The only cost is the roughly 5GB download at 4-bit quantization and the laptop you already own. --- Canonical HTML: https://runatlas.sh/resources/models/qwen2-5-coder-local Source of truth: aeo_pages row `/resources/models/qwen2-5-coder-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.