What is the context window size for Qwen3 30B-A3B (Ollama) in Atlas?

Qwen3 30B-A3B (Ollama) provides a 256K token (262,144) context window for its instruct and thinking tags when properly configured in Atlas. It is crucial to set `limit.context` to `262144` in your `atlas.json` to access this full capacity.

Is Qwen3 30B-A3B (Ollama) free to use with Atlas?

Yes, Qwen3 30B-A3B (Ollama) is Free (self-hosted) when used with Atlas. This means there are no ongoing token costs, only the initial hardware requirement of approximately 21GB of VRAM or unified memory to host the model locally.

How much memory does Qwen3 30B-A3B (Ollama) require?

Qwen3 30B-A3B (Ollama) requires 19GB of memory for its weights. To serve it effectively, you will need roughly 21GB of VRAM or unified memory free, as all experts in the mixture-of-experts model must be resident.

Can Atlas use Qwen3 30B-A3B (Ollama) for local code indexing?

Yes, Atlas can build its code index with local Ollama embeddings, including those from Qwen3 30B-A3B (Ollama). This ensures that AST chunks, vectors, and prompts all stay local, keeping your code off third-party servers.

What is the performance benefit of Qwen3 30B-A3B (Ollama)'s architecture?

Qwen3 30B-A3B (Ollama) is a mixture-of-experts model with 30B total parameters but only about 3B active per token. This architecture provides inference speed closer to a small model, offering the single best throughput trade available for a local Atlas agent.

Are there different versions of Qwen3 30B-A3B (Ollama) for reasoning?

Yes, Qwen3 30B-A3B (Ollama) offers instruct and thinking variants at the same 19GB memory footprint. This allows Atlas users to toggle between different reasoning modes by swapping the model tag, without needing to change hardware.

Atlas with Qwen3 30B-A3B (Ollama) in 2026

Qwen3 30B-A3B (Ollama) is an excellent choice for Atlas users seeking a powerful, self-hosted AI coding agent experience in 2026. This model provides a 256K token context window and operates entirely Free (self-hosted), offering a unique mixture-of-experts architecture that balances 30B total parameters with the inference speed of a much smaller model.

What is Qwen3 30B-A3B (Ollama) best for with Atlas?

Qwen3 30B-A3B (Ollama) offers Atlas users in 2026 an unparalleled balance of performance and local control, leveraging its 30B total parameters with only about 3B active per token. This mixture-of-experts model provides inference speeds closer to a smaller model while maintaining the capabilities of a larger one, all for Free (self-hosted).

Qwen3 30B-A3B (Ollama) excels as the single best throughput trade available for a local Atlas agent. Its unique architecture means that while it has 30B total parameters, only approximately 3B are active per token, delivering inference speed comparable to a much smaller model. This makes it ideal for developers who need robust AI capabilities directly within their terminal, without relying on third-party servers. Atlas can build its code index with local Ollama embeddings, ensuring that code remains off third-party servers, a critical feature for privacy-conscious workflows. The model's 256K token context window is substantial enough to hold a serious slice of a repository, allowing Atlas to perform deep code analysis, draft plans in a read-only plan agent, and compute unified diffs for file edits before approval. Atlas also uses Axis, the hybrid semantic and keyword code search, which benefits from local embeddings provided by Ollama.

What are the memory and context window tradeoffs for Qwen3 30B-A3B (Ollama)?

Qwen3 30B-A3B (Ollama) provides a generous 256K token context window, but its mixture-of-experts architecture requires a significant 19GB of memory for the weights. This means that while active parameter count is low, the model still wants roughly 21GB of VRAM or unified memory to serve effectively, as all experts must be resident.

The primary tradeoff for Qwen3 30B-A3B (Ollama) is its memory footprint. Despite having only about 3B active parameters per token, the full 30B parameter mixture-of-experts model still needs all 19GB of its weights resident in memory. This translates to a requirement of roughly 21GB of VRAM or unified memory to ensure smooth operation within Atlas. Developers must confirm they have this memory free before deployment. Another important consideration is the context window discrepancy: while the instruct and thinking tags for Qwen3 30B-A3B (Ollama) support 256K tokens (262,144), the plain 30b-a3b-q4_K_M tag in Ollama might show only 40K tokens. Atlas users must explicitly check the tag they pulled and configure `limit.context` to 262144 in their `atlas.json` to leverage the full 256K context window. This careful tag selection is crucial to avoid unexpected context limitations during agent operations.

When should I choose a different model over Qwen3 30B-A3B (Ollama) for Atlas?

While Qwen3 30B-A3B (Ollama) offers a compelling Free (self-hosted) solution with a 256K context window, it may not be the optimal choice if your hardware cannot provide the required 21GB of VRAM or unified memory. Developers with more constrained local environments might need to consider smaller models or cloud-hosted alternatives.

Developers should consider a different model if their local machine lacks the necessary 21GB of VRAM or unified memory to host Qwen3 30B-A3B (Ollama). Although its active parameter count is low, the full model weights must be resident, making it unsuitable for systems with less memory. Additionally, if the specific task requires an even larger context window than 256K tokens, or if the developer prefers to offload compute to a cloud provider for convenience or scale, other models integrated with Atlas might be more appropriate. Atlas lets you switch the active model and provider on the fly, making it easy to experiment with different configurations. For scenarios where the 40K context window of the plain `30b-a3b-q4_K_M` tag is accidentally used, a different model or a correctly tagged Qwen3 variant would be necessary to avoid truncated interactions with Atlas's code indexing and planning capabilities.

How does Qwen3 30B-A3B (Ollama) enhance Atlas's coding agent capabilities?

Qwen3 30B-A3B (Ollama) significantly enhances Atlas's capabilities by providing a powerful local reasoning engine with a 256K token context window, allowing the agent to process extensive codebases. Its instruct and thinking variants, available at the same 19GB memory footprint, enable developers to toggle reasoning modes without hardware changes.

With Qwen3 30B-A3B (Ollama), Atlas gains a robust local AI backend capable of handling complex coding tasks. The 256K context window allows Atlas to ingest and understand large portions of a repository, facilitating advanced operations like searching code with Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion. Atlas indexes code by AST declarations using tree-sitter, not blind line windows, and Qwen3's large context supports this detailed structural understanding. The availability of instruct and thinking variants at the same 19GB memory cost means Atlas can switch between different reasoning styles by simply swapping the model tag, optimizing its approach for drafting plans in the read-only plan agent or generating code in the build agent. Every Atlas tool call is permission-gated, and the model's local nature ensures that sensitive code remains on the developer's machine, aligning with Atlas's commitment to privacy and control.

What are the pricing benefits of Qwen3 30B-A3B (Ollama) with Atlas?

Qwen3 30B-A3B (Ollama) offers a significant pricing benefit for Atlas users in 2026: it is entirely Free (self-hosted). This eliminates ongoing token costs associated with cloud-based models, making it an economically attractive option for developers who can meet its 19GB memory requirement.

The primary pricing benefit of Qwen3 30B-A3B (Ollama) is its Free (self-hosted) nature. This means that once the model is pulled and configured, there are no additional costs for inference, regardless of usage volume. For developers leveraging Atlas for extensive code analysis, refactoring, or new feature development, this translates into substantial savings compared to pay-per-token cloud services. The only 'cost' is the initial hardware investment to ensure at least 21GB of VRAM or unified memory is available. This makes Qwen3 30B-A3B (Ollama) an ideal choice for long-term, intensive local development with Atlas, allowing for unlimited experimentation and iteration without budget constraints. Atlas's ability to switch models on the fly means developers can use this Free local option as a default and only switch to paid cloud models when specific, high-demand capabilities are required.

Setup

01Pull the Qwen3 30B-A3B (Ollama) model: Use the command `ollama pull qwen3:30b-a3b`. This will download the 19GB mixture-of-experts model, including its instruct and thinking tags.
02Verify memory availability: Confirm you have roughly 21GB of VRAM or unified memory free. All experts must be resident for Qwen3 30B-A3B (Ollama) to function correctly, even though few fire per token.
03Configure Atlas model map: Register `qwen3:30b-a3b` in your `atlas.json` Ollama models map. Set `limit.context` to `262144` and `limit.output` to `8192` to utilize the full 256K token context window.
04Set active model in Atlas: In the Atlas TUI, set the active model to `ollama/qwen3:30b-a3b`. Run `/models` to add it to your favorites for fast switching between models.
05Index repository with Ollama embedder: Index your code repository with the Ollama embedder. This ensures that AST chunks, vectors, and prompts all stay local, maintaining privacy and leveraging the local model.

Frequently asked questions

What is the context window size for Qwen3 30B-A3B (Ollama) in Atlas?: Qwen3 30B-A3B (Ollama) provides a 256K token (262,144) context window for its instruct and thinking tags when properly configured in Atlas. It is crucial to set `limit.context` to `262144` in your `atlas.json` to access this full capacity.
Is Qwen3 30B-A3B (Ollama) free to use with Atlas?: Yes, Qwen3 30B-A3B (Ollama) is Free (self-hosted) when used with Atlas. This means there are no ongoing token costs, only the initial hardware requirement of approximately 21GB of VRAM or unified memory to host the model locally.
How much memory does Qwen3 30B-A3B (Ollama) require?: Qwen3 30B-A3B (Ollama) requires 19GB of memory for its weights. To serve it effectively, you will need roughly 21GB of VRAM or unified memory free, as all experts in the mixture-of-experts model must be resident.
Can Atlas use Qwen3 30B-A3B (Ollama) for local code indexing?: Yes, Atlas can build its code index with local Ollama embeddings, including those from Qwen3 30B-A3B (Ollama). This ensures that AST chunks, vectors, and prompts all stay local, keeping your code off third-party servers.
What is the performance benefit of Qwen3 30B-A3B (Ollama)'s architecture?: Qwen3 30B-A3B (Ollama) is a mixture-of-experts model with 30B total parameters but only about 3B active per token. This architecture provides inference speed closer to a small model, offering the single best throughput trade available for a local Atlas agent.
Are there different versions of Qwen3 30B-A3B (Ollama) for reasoning?: Yes, Qwen3 30B-A3B (Ollama) offers instruct and thinking variants at the same 19GB memory footprint. This allows Atlas users to toggle between different reasoning modes by swapping the model tag, without needing to change hardware.

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