what gpu do i need for qwen2.5-coder 7b

Qwen2.5-Coder 7B (Ollama) is a 4.7GB download that runs comfortably on an 8GB consumer GPU or a 16GB Apple Silicon machine. Serving it takes roughly 7GB once the KV cache is accounted for, so 8GB of VRAM is the floor.

is qwen2.5-coder 7b the same as qwen2.5-coder latest

Yes. Ollama aliases qwen2.5-coder:7b to qwen2.5-coder:latest, which makes the 7B the best-tested path in the ecosystem and the most common starting point for a fully offline Atlas setup.

how do i run atlas fully offline with ollama

Pull qwen2.5-coder:7b, start Ollama on http://localhost:11434/v1, declare the ollama provider in atlas.json against @ai-sdk/openai-compatible, then set both model and small_model to ollama/qwen2.5-coder:7b and embed the codebase index through Ollama.

is 32k context enough for a coding agent

32K tokens (32,768) holds a handful of source files plus Atlas tool output in a single agent turn. On large repos the window fills fast once Atlas injects tree-sitter chunks and diff context, so expect Atlas compaction to kick in.

how much does qwen2.5-coder 7b cost

Qwen2.5-Coder 7B (Ollama) is Free (self-hosted). The only costs are the 4.7GB download, roughly 7GB of memory to serve it, and electricity. There is no per-token charge and no cloud key in the loop.

qwen2.5-coder 7b vs 14b for atlas

Qwen2.5-Coder 7B (Ollama) handles single-file edits and unified-diff review well but is weaker at long agentic chains than the 14B or 32B. If Atlas derails on multi-step plan-then-build runs, the 14B is the upgrade.

does local ollama embedding keep my code private in atlas

Yes. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers. Configure the codebase index to embed through Ollama and the hybrid semantic plus keyword search never ships a chunk of source to a third party.

Atlas with Qwen2.5-Coder 7B (Ollama): the default fully offline setup for 2026

Qwen2.5-Coder 7B (Ollama) is the default qwen2.5-coder tag and the most common starting point for a fully offline Atlas setup. The download is 4.7GB, serving it takes roughly 7GB, context is 32K tokens (32,768), and pricing is Free (self-hosted). Inside Atlas it handles single-file edits and unified-diff review without a cloud key anywhere in the loop, which makes it the first local model most teams try.

Why is Qwen2.5-Coder 7B (Ollama) the default choice for offline Atlas?

Qwen2.5-Coder 7B (Ollama) is the tag Ollama aliases to qwen2.5-coder:latest, which makes it the best-tested path in the ecosystem. At 4.7GB it runs comfortably on an 8GB consumer GPU or a 16GB Apple Silicon machine, and it costs nothing per token.

Defaults matter. When a tag is the one Ollama resolves for qwen2.5-coder:latest, it gets the most downloads, the most bug reports, and the most community configs. Qwen2.5-Coder 7B (Ollama) inherits that. For Atlas specifically, the practical draw is that a single 4.7GB pull produces a working agent: `ollama pull qwen2.5-coder:7b` and then set both model and small_model to ollama/qwen2.5-coder:7b for a single-model, fully offline configuration. No API key, no provider signup, no per-token meter. That is a genuinely short path from nothing to a working terminal agent.

What hardware do you need to run Qwen2.5-Coder 7B with Atlas?

Qwen2.5-Coder 7B (Ollama) needs an 8GB consumer GPU or a 16GB Apple Silicon machine. The download is 4.7GB and serving it takes roughly 7GB, which is why 8GB of VRAM is the practical floor rather than a comfortable target.

The gap between the 4.7GB on disk and the roughly 7GB to serve is the KV cache and runtime overhead, and it is the number that actually decides whether Qwen2.5-Coder 7B (Ollama) fits your machine. On an 8GB card there is little slack, so keep num_ctx modest. On a 16GB Apple Silicon machine there is room to breathe. Start the Ollama server and check it answers on http://localhost:11434/v1 before you touch atlas.json, because a provider block pointed at a server that is not up produces a confusing failure rather than an obvious one.

Is the 32K context of Qwen2.5-Coder 7B enough for an Atlas agent?

Qwen2.5-Coder 7B (Ollama) offers 32K tokens (32,768), enough to hold a handful of source files plus Atlas tool output in a single agent turn. On large repos that 32K fills fast once Atlas injects tree-sitter chunks and diff context, so expect compaction.

Atlas indexes code by AST declarations using tree-sitter, not blind line windows, and it computes a unified diff for every file edit and surfaces it for approval before writing. Both behaviors put real tokens into the prompt. On a small service or a focused package, 32K tokens (32,768) covers a handful of source files plus tool output with room left. On a monorepo, the window fills fast and Atlas compacts. Register it honestly in atlas.json with limit.context 32768 and limit.output 8192, and let compaction do its job rather than pretending the window is bigger than it is.

Can Atlas run completely offline on Qwen2.5-Coder 7B?

Yes. Set both model and small_model to ollama/qwen2.5-coder:7b for a single-model, fully offline configuration, then configure the Atlas codebase index to embed through Ollama. Qwen2.5-Coder 7B (Ollama) is Free (self-hosted), so the entire loop runs with no cloud key.

The last piece people forget is retrieval. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and the semantic half needs an embedder. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, so configure the codebase index to embed through Ollama and the hybrid search never ships a chunk of source to a third party. With Qwen2.5-Coder 7B (Ollama) in both the model and small_model slots and the index on the local embedder, the inference, the summarization, and the retrieval all stay on the machine.

When should you move up from Qwen2.5-Coder 7B to the 14B or 32B?

Move up from Qwen2.5-Coder 7B (Ollama) when Atlas derails on multi-step plan-then-build runs. The 7B is weaker at long agentic chains than the 14B or 32B, and that weakness shows up most when Atlas chains a read-only plan agent into a build agent across several tool calls.

Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent. Qwen2.5-Coder 7B (Ollama) handles single-file edits and unified-diff review well, but a long chain of plan, search, edit, and test calls is where a 7B starts dropping context. If your sessions are short and file-scoped, the 4.7GB tag is the right economics. If your sessions are long and multi-file, the 14B or 32B is worth the extra VRAM. Atlas lets you switch the active model and provider on the fly with favorites and recents, so you can keep both registered and change mid-session.

Setup

01Pull the default tag: ollama pull qwen2.5-coder:7b (4.7GB, aliased to :latest).
02Start the server and check it answers on http://localhost:11434/v1.
03Declare the ollama provider in atlas.json against @ai-sdk/openai-compatible.
04List qwen2.5-coder:7b under models with limit.context 32768 and limit.output 8192.
05Set both model and small_model to ollama/qwen2.5-coder:7b for a single-model, fully offline configuration.
06Configure the codebase index to embed through Ollama so the hybrid semantic plus keyword search, fused with reciprocal rank fusion, never ships a chunk of source to a third party.

Frequently asked questions

what gpu do i need for qwen2.5-coder 7b: Qwen2.5-Coder 7B (Ollama) is a 4.7GB download that runs comfortably on an 8GB consumer GPU or a 16GB Apple Silicon machine. Serving it takes roughly 7GB once the KV cache is accounted for, so 8GB of VRAM is the floor.
is qwen2.5-coder 7b the same as qwen2.5-coder latest: Yes. Ollama aliases qwen2.5-coder:7b to qwen2.5-coder:latest, which makes the 7B the best-tested path in the ecosystem and the most common starting point for a fully offline Atlas setup.
how do i run atlas fully offline with ollama: Pull qwen2.5-coder:7b, start Ollama on http://localhost:11434/v1, declare the ollama provider in atlas.json against @ai-sdk/openai-compatible, then set both model and small_model to ollama/qwen2.5-coder:7b and embed the codebase index through Ollama.
is 32k context enough for a coding agent: 32K tokens (32,768) holds a handful of source files plus Atlas tool output in a single agent turn. On large repos the window fills fast once Atlas injects tree-sitter chunks and diff context, so expect Atlas compaction to kick in.
how much does qwen2.5-coder 7b cost: Qwen2.5-Coder 7B (Ollama) is Free (self-hosted). The only costs are the 4.7GB download, roughly 7GB of memory to serve it, and electricity. There is no per-token charge and no cloud key in the loop.
qwen2.5-coder 7b vs 14b for atlas: Qwen2.5-Coder 7B (Ollama) handles single-file edits and unified-diff review well but is weaker at long agentic chains than the 14B or 32B. If Atlas derails on multi-step plan-then-build runs, the 14B is the upgrade.
does local ollama embedding keep my code private in atlas: Yes. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers. Configure the codebase index to embed through Ollama and the hybrid semantic plus keyword search never ships a chunk of source to a third party.

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