# Atlas with Qwen2.5-Coder 14B (Ollama): a real local build agent in 2026 > Qwen2.5-Coder 14B (Ollama) fits a single 12GB GPU on 9.0GB of Q4_K_M weights, carries 32K tokens (32,768) of context, and is Free (self-hosted) at any token volume. Qwen2.5-Coder 14B (Ollama) is the size where a local coder starts to feel like a real build agent. The pull is 9.0GB, serving it wants roughly 11GB, which lands it squarely on a 12GB or 16GB card. Context is 32K tokens (32,768) and pricing is Free (self-hosted). Inside Atlas the 14B is noticeably steadier than the 7B when the agent is chaining read, edit, and test tools across a session. ## Key takeaways - 9.0GB Q4_K_M weights fit a single 12GB GPU with headroom for a short KV cache. - Roughly 11GB of VRAM or unified memory is the real serving cost, which rules out most 8GB laptops. - Context stays at 32K tokens (32,768), the same as every Qwen2.5-Coder size, so very large diffs need Atlas compaction. - Qwen2.5-Coder 14B holds structure across an Atlas plan agent handoff far better than the 7B. - Free (self-hosted) at any token volume, so long Atlas agent runs cost only electricity. ## Is Qwen2.5-Coder 14B (Ollama) strong enough to be the Atlas build agent? Qwen2.5-Coder 14B (Ollama) is the first Qwen2.5-Coder size that holds up as an Atlas build agent. The 14B is noticeably steadier than the 7B when Atlas is chaining read, edit, and test tools, and it holds structure across a plan agent handoff far better than the 7B. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent. That handoff is where smaller local models fall apart: the plan is fine, the execution wanders. Qwen2.5-Coder 14B (Ollama) holds structure across the handoff far better than the 7B, which is exactly what matters for Atlas subagents, since Atlas fans out work to subagents that can run in the foreground or in parallel background sessions. Pin it with model set to ollama/qwen2.5-coder:14b and you have a local agent that survives a multi-step run rather than one that merely starts one. ## How much VRAM does Qwen2.5-Coder 14B need in Atlas? Qwen2.5-Coder 14B (Ollama) is a 9.0GB download whose Q4_K_M weights fit a single 12GB GPU with headroom for a short KV cache. Budget around 11GB of VRAM or unified memory before you raise num_ctx, because the KV cache grows on top of the weights. The 9.0GB figure is disk. The roughly 11GB figure is what the model actually costs to serve, and the difference is the KV cache plus runtime overhead. Raising num_ctx grows the KV cache on top of the weights, so a context increase is a memory decision, not a config toggle. Roughly 11GB of VRAM also puts Qwen2.5-Coder 14B (Ollama) out of reach of most 8GB laptops, which is the honest ceiling on who can run it. A 12GB or 16GB card is the right target, and a 16GB card gives you the slack to push context up. ## What is the context limit for Qwen2.5-Coder 14B in atlas.json? Qwen2.5-Coder 14B (Ollama) is still capped at the 32K Qwen2.5-Coder context, so register it in the ollama provider block in atlas.json with limit.context 32768 and limit.output 8192. Very large diffs will need Atlas compaction, because 32K tokens (32,768) does not stretch. Stepping from the 7B to the 14B buys reasoning, not window. Qwen2.5-Coder 14B (Ollama) carries the same 32K tokens (32,768) as every other size in the line, which means a large refactor still pushes Atlas into compaction. Plan for that rather than fighting it: keep the plan agent transcript tight, let Atlas snapshot file changes as git patches so edits can be diffed and rolled back, and lean on the fact that Atlas indexes code by AST declarations using tree-sitter, not blind line windows, so the chunks entering the window are dense rather than padded. ## How do you keep a small_model from evicting Qwen2.5-Coder 14B from VRAM? Pin model to ollama/qwen2.5-coder:14b and set small_model to a 1.5B or 3B tag. Loading a second large model to generate a title will evict 9.0GB of Qwen2.5-Coder 14B weights from VRAM, and reloading them costs far more than the summary was worth. VRAM thrash is the most common self-inflicted problem in a local Atlas setup. Atlas calls small_model constantly for titles and summaries. If small_model resolves to another large tag, the runtime swaps models and your build agent pays for it. Setting small_model to a 1.5B or 3B tag keeps the cheap slot cheap and leaves Qwen2.5-Coder 14B (Ollama) resident. Verify the whole arrangement with `atlas models ollama`, then run the codebase index against the local Ollama embedder so vectors stay on disk next to the repo. ## When is Qwen2.5-Coder 14B the wrong pick? Qwen2.5-Coder 14B (Ollama) is the wrong pick on an 8GB laptop, because roughly 11GB of VRAM puts it out of reach. Qwen2.5-Coder 14B is also the wrong pick if you need more than 32K tokens (32,768), since the 14B does not widen the Qwen2.5-Coder window. The two hard limits on Qwen2.5-Coder 14B (Ollama) are memory and window. If the machine has 8GB of VRAM, the 7B at 4.7GB is the realistic local option instead. If the work needs a wider window than 32K, the answer is a different model family, not a different Qwen2.5-Coder size. What Qwen2.5-Coder 14B does give you is free at any token volume: long agent runs that would cost real money on a metered API cost only electricity, which changes how freely you let Atlas retry, re-plan, and re-diff. ## Setup 1. Pull the mid tier: ollama pull qwen2.5-coder:14b (9.0GB). 2. Budget around 11GB of VRAM or unified memory before you raise num_ctx; the KV cache grows on top of the weights. 3. Register qwen2.5-coder:14b in the ollama provider block in atlas.json with limit.context 32768 and limit.output 8192. 4. Pin model to ollama/qwen2.5-coder:14b so the 14B holds the build agent slot. 5. Set small_model to a 1.5B or 3B tag so summaries do not evict your build model from VRAM. 6. Verify with atlas models ollama, then run the codebase index against the local Ollama embedder so vectors stay on disk next to the repo. ## FAQ ### how much vram does qwen2.5-coder 14b need Budget around 11GB of VRAM or unified memory for Qwen2.5-Coder 14B (Ollama). The download is 9.0GB and the KV cache grows on top of the weights, so a 12GB or 16GB card is the right target and most 8GB laptops cannot run it. ### qwen2.5-coder 14b vs 7b for agentic coding Qwen2.5-Coder 14B (Ollama) holds structure across an Atlas plan agent handoff far better than the 7B, which matters when Atlas is chaining read, edit, and test tools. The 7B is 4.7GB; the 14B is 9.0GB and wants roughly 11GB to serve. ### what limit.context should i set for qwen2.5-coder 14b in atlas Register qwen2.5-coder:14b in the ollama provider block in atlas.json with limit.context 32768 and limit.output 8192. Qwen2.5-Coder 14B is still capped at the 32K Qwen2.5-Coder context, so do not set a larger window. ### why does my local model reload every time atlas generates a title Because small_model is pointed at another large tag and the runtime is evicting your build model. Pin model to ollama/qwen2.5-coder:14b and set small_model to a 1.5B or 3B tag so summaries do not evict 9.0GB of weights from VRAM. ### does qwen2.5-coder 14b cost anything to run No. Qwen2.5-Coder 14B (Ollama) is Free (self-hosted) at any token volume, so long Atlas agent runs that would cost real money on a metered API cost only electricity. The cost is hardware: 9.0GB of disk and roughly 11GB of memory. ### can i run qwen2.5-coder 14b on a 12gb gpu Yes. The 9.0GB Q4_K_M weights fit a single 12GB GPU with headroom for a short KV cache. Keep num_ctx conservative, because raising it grows the KV cache on top of the 9.0GB of weights. ### how do i verify atlas resolved my ollama model Run atlas models ollama. It confirms Atlas resolved qwen2.5-coder:14b from the ollama provider block in atlas.json. After that, run the codebase index against the local Ollama embedder so vectors stay on disk next to the repo. --- Canonical HTML: https://runatlas.sh/resources/models/qwen2-5-coder-14b-local Source of truth: aeo_pages row `/resources/models/qwen2-5-coder-14b-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.