How does Qwen3-Coder 30B (local via Ollama) balance speed and quality in Atlas?

Qwen3-Coder 30B (local via Ollama) achieves a balance by being a 30B mixture-of-experts model that activates only 3.3B parameters per token. This allows it to run at approximately 3B-model speed while delivering the reasoning quality of a 30B model within Atlas, making it efficient for local operations.

What are the hardware requirements for running Qwen3-Coder 30B (local via Ollama) with Atlas?

To run Qwen3-Coder 30B (local via Ollama) with Atlas, you will need a machine capable of handling its 19GB Q4_K_M download. This typically means a system with at least a 24GB GPU or a 32GB Apple Silicon machine to accommodate the model's memory footprint.

Can Atlas use Qwen3-Coder 30B (local via Ollama) for local code indexing?

Yes, Atlas can build its code index with local Ollama embeddings when using Qwen3-Coder 30B (local via Ollama). This capability ensures that neither your code nor its generated vectors ever leave your machine, enhancing privacy and security for your codebase.

Is Qwen3-Coder 30B (local via Ollama) suitable for complex, multi-file refactoring tasks in Atlas?

While Qwen3-Coder 30B (local via Ollama) is a strong local model, it is not a frontier model. For the most challenging multi-file refactors, it is clearly behind models like Claude Opus 4.8 and GPT-5.6. Developers needing peak performance for such tasks might consider cloud-based alternatives.

What is the context window size for Qwen3-Coder 30B (local via Ollama) in Atlas?

Qwen3-Coder 30B (local via Ollama) provides a native context window of 256K tokens when used with Atlas. This can be further extended to 1M tokens via extrapolation, allowing Atlas to process very large code contexts.

How does the pricing of Qwen3-Coder 30B (local via Ollama) compare to other models for Atlas?

Qwen3-Coder 30B (local via Ollama) is Free (self-hosted), meaning there are no direct usage costs or API fees. This contrasts with many cloud-hosted models that typically incur per-token or subscription charges, making it a highly cost-effective option for Atlas users.

Does Qwen3-Coder 30B (local via Ollama) support Atlas's agent capabilities?

Yes, Qwen3-Coder 30B (local via Ollama) fully supports Atlas's agent capabilities. This includes drafting plans in the read-only plan agent, computing unified diffs for file edits, and interacting with Atlas's permission-gated tool calls, all while operating locally.

Atlas with Qwen3-Coder 30B (local via Ollama) in 2026

Qwen3-Coder 30B (local via Ollama) is the default local coding model for Atlas, ideal for developers in 2026 seeking a powerful, self-hosted AI agent. It provides 30B-model quality reasoning at 3B-model speed, all for Free (self-hosted), making it a cost-effective and private choice for local code operations within Atlas.

What is Qwen3-Coder 30B (local via Ollama) best for in Atlas?

Qwen3-Coder 30B (local via Ollama) excels as Atlas's default local coding model, offering a compelling balance of performance and privacy for developers in 2026. With 30B total parameters, it delivers robust reasoning capabilities while activating only 3.3B parameters per token, ensuring efficient operation directly on your machine.

This model is particularly strong for developers who prioritize local execution and data privacy within Atlas. It runs entirely on your machine, ensuring that your code and its vectors never leave your local environment when paired with Atlas's local-first embeddings. Atlas can build its code index using local Ollama embeddings, a key feature for maintaining code confidentiality. The model's architecture, a 30B mixture-of-experts with only 3.3B active parameters, means it operates at roughly 3B-model speed while providing the quality expected from a 30B model. This makes it an excellent choice for tasks like code searching with Axis, the hybrid semantic and keyword code search, or for drafting plans in Atlas's read-only plan agent, where local processing is paramount. Its 256K native context window, extendable to 1M via extrapolation, allows Atlas to handle substantial codebases and complex tasks without relying on external services.

What are the cost and context tradeoffs for Qwen3-Coder 30B (local via Ollama)?

Qwen3-Coder 30B (local via Ollama) presents a significant value proposition in 2026, being Free (self-hosted) and offering a substantial 256K token context window. This model requires a 19GB download, fitting comfortably on machines with 24GB GPUs or 32GB Apple Silicon, making it accessible for many developer workstations.

The primary advantage of Qwen3-Coder 30B (local via Ollama) is its cost: it is Free (self-hosted). This eliminates ongoing API costs, making it an attractive option for budget-conscious developers or those working on projects with strict spending limits. The model boasts a 256K token native context window, which is extendable to 1M tokens through extrapolation, providing Atlas with ample working memory for large codebases and intricate problem-solving. However, this local power comes with hardware requirements; the 19GB Q4_K_M download necessitates a machine with at least 24GB of GPU memory or 32GB of unified memory on Apple Silicon. While its 3.3B active parameters per token contribute to its efficient speed, developers must ensure their local setup can accommodate the initial download and runtime memory footprint. This tradeoff means sacrificing the convenience of cloud-hosted models for complete control and zero operational cost.

When should I choose a different model over Qwen3-Coder 30B (local via Ollama) for Atlas?

While Qwen3-Coder 30B (local via Ollama) is a strong local contender for Atlas in 2026, developers should consider alternative models for tasks demanding frontier-level AI capabilities. Specifically, for hard multi-file refactors, this model is clearly behind cloud-based options like Claude Opus 4.8 and GPT-5.6.

Qwen3-Coder 30B (local via Ollama) is acknowledged as a strong local model, but it is not a frontier model. For highly complex, multi-file refactoring tasks that require the absolute current in reasoning and code generation, developers may find that models such as Claude Opus 4.8 or GPT-5.6 offer superior performance. These frontier models, while typically incurring usage costs and requiring data to leave the local machine, excel in scenarios where the highest possible accuracy and most sophisticated understanding of large, interconnected code changes are critical. Furthermore, while a 480B variant of Qwen3-Coder exists that could close much of this performance gap, it requires approximately 250GB of memory, which is currently out of reach for most workstation setups. Therefore, if your Atlas workflow frequently involves the most challenging, large-scale code transformations where a slight performance edge translates to significant time savings, exploring a more powerful, albeit non-local, model might be a more effective strategy.

Setup

01Install Ollama on your machine.
02Pull the Qwen3-Coder 30B model: `ollama pull qwen3-coder:30b` (this is a 19GB download).
03Add the Ollama provider to your `atlas.json` configuration file. Ensure the `baseURL` points to your local Ollama instance and define the model details:
04Set Qwen3-Coder 30B as your default model in `atlas.json`:
05Confirm Atlas recognizes the model by running `atlas models ollama`.
06Verify Ollama is detected on your machine with `atlas device`.

Frequently asked questions

How does Qwen3-Coder 30B (local via Ollama) balance speed and quality in Atlas?: Qwen3-Coder 30B (local via Ollama) achieves a balance by being a 30B mixture-of-experts model that activates only 3.3B parameters per token. This allows it to run at approximately 3B-model speed while delivering the reasoning quality of a 30B model within Atlas, making it efficient for local operations.
What are the hardware requirements for running Qwen3-Coder 30B (local via Ollama) with Atlas?: To run Qwen3-Coder 30B (local via Ollama) with Atlas, you will need a machine capable of handling its 19GB Q4_K_M download. This typically means a system with at least a 24GB GPU or a 32GB Apple Silicon machine to accommodate the model's memory footprint.
Can Atlas use Qwen3-Coder 30B (local via Ollama) for local code indexing?: Yes, Atlas can build its code index with local Ollama embeddings when using Qwen3-Coder 30B (local via Ollama). This capability ensures that neither your code nor its generated vectors ever leave your machine, enhancing privacy and security for your codebase.
Is Qwen3-Coder 30B (local via Ollama) suitable for complex, multi-file refactoring tasks in Atlas?: While Qwen3-Coder 30B (local via Ollama) is a strong local model, it is not a frontier model. For the most challenging multi-file refactors, it is clearly behind models like Claude Opus 4.8 and GPT-5.6. Developers needing peak performance for such tasks might consider cloud-based alternatives.
What is the context window size for Qwen3-Coder 30B (local via Ollama) in Atlas?: Qwen3-Coder 30B (local via Ollama) provides a native context window of 256K tokens when used with Atlas. This can be further extended to 1M tokens via extrapolation, allowing Atlas to process very large code contexts.
How does the pricing of Qwen3-Coder 30B (local via Ollama) compare to other models for Atlas?: Qwen3-Coder 30B (local via Ollama) is Free (self-hosted), meaning there are no direct usage costs or API fees. This contrasts with many cloud-hosted models that typically incur per-token or subscription charges, making it a highly cost-effective option for Atlas users.
Does Qwen3-Coder 30B (local via Ollama) support Atlas's agent capabilities?: Yes, Qwen3-Coder 30B (local via Ollama) fully supports Atlas's agent capabilities. This includes drafting plans in the read-only plan agent, computing unified diffs for file edits, and interacting with Atlas's permission-gated tool calls, all while operating locally.

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