What hardware do I need to run Qwen2.5-Coder 32B (Ollama) with Atlas?

To run Qwen2.5-Coder 32B (Ollama) effectively with Atlas, you will need a GPU with at least 24GB of VRAM, such as an NVIDIA 3090, 4090, or AMD 7900 XTX, or a Mac with 32GB or more unified memory. The model requires roughly 22GB to serve.

Is Qwen2.5-Coder 32B (Ollama) truly air-gapped when used with Atlas?

Yes, Qwen2.5-Coder 32B (Ollama) is air-gapped by construction. When configured with Atlas and the local Ollama embedder, all model weights, prompts, diffs, and the codebase's vector index remain entirely on your local machine, never leaving your network.

What is the context window size for Qwen2.5-Coder 32B (Ollama) in Atlas?

Qwen2.5-Coder 32B (Ollama) provides a 32K tokens (32,768) context window when used with Atlas. This is a robust size for many local coding tasks.

How does Qwen2.5-Coder 32B (Ollama) compare to smaller Qwen2.5-Coder models for Atlas?

Qwen2.5-Coder 32B (Ollama) is the largest and strongest fully offline coding model in its series that most people can run. It handles Atlas's plan-then-build flows and permission-gated tool chains with significantly fewer derailments compared to the 7B or 14B versions.

Will using a long context with Qwen2.5-Coder 32B (Ollama) impact performance on a 24GB GPU?

Yes, while the model fits on a 24GB card, its roughly 22GB serving requirement means that utilizing a very long context, approaching the 32K token limit, will likely push the system into VRAM offload, which can reduce throughput and increase response times.

Can Atlas switch between Qwen2.5-Coder 32B (Ollama) and other models?

Yes, Atlas is designed to let you switch the active model and provider on the fly. This allows you to use Qwen2.5-Coder 32B (Ollama) for local, air-gapped work and easily switch to other models, including cloud-based ones, for tasks requiring different capabilities or larger contexts.

Is Qwen2.5-Coder 32B (Ollama) free to use with Atlas?

Yes, Qwen2.5-Coder 32B (Ollama) is free (self-hosted), meaning there are no direct costs associated with using the model itself, beyond your own hardware and electricity.

Atlas with Qwen2.5-Coder 32B (Ollama) in 2026

Qwen2.5-Coder 32B (Ollama) is an excellent choice for Atlas users in 2026 seeking a powerful, fully offline AI coding agent that runs locally on consumer GPUs. This model, available for free (self-hosted), excels at handling complex Atlas plan-then-build workflows and permission-gated tool chains, keeping all your code and interactions air-gapped on your machine with its 32K token context window.

What is Qwen2.5-Coder 32B (Ollama) best for with Atlas?

Qwen2.5-Coder 32B (Ollama) is the strongest fully offline coding model most Atlas developers can run on a single consumer GPU in 2026. With 20GB Q4_K_M weights, it fills a 24GB card optimally for code, handling complex plan-then-build flows with fewer derailments than smaller 7B or 14B models.

This model is specifically designed for robust local operation within Atlas. It excels at managing Atlas's sophisticated workflows, including the read-only plan agent and subsequent build agent, as well as permission-gated tool calls. Its ability to keep weights, prompts, diffs, and even the vector index entirely on your machine, especially when paired with the local Ollama embedder, ensures an air-gapped development environment. This makes Qwen2.5-Coder 32B (Ollama) ideal for sensitive projects where code must never leave your local network. Atlas's ability to switch models on the fly means you can leverage this powerful local option for core development tasks.

What are the cost and context window tradeoffs for Qwen2.5-Coder 32B (Ollama)?

Qwen2.5-Coder 32B (Ollama) offers a compelling value proposition in 2026, being entirely free (self-hosted) for Atlas users. However, this local power comes with hardware considerations: it requires roughly 22GB of VRAM to serve, meaning a 24GB consumer GPU or a 32GB-plus Mac is essential.

While the free (self-hosted) pricing is a significant advantage, the model's 22GB serving requirement means that on a 24GB card, utilizing its full 32K tokens (32,768) context window for very long interactions will likely push the system into offload, significantly impacting throughput. This can lead to slower response times compared to cloud-hosted models with larger VRAM allocations. Furthermore, its 32K token context window, while substantial for many tasks, is considered small when compared to the much larger context windows offered by modern cloud-based coding models available in 2026. Atlas users must weigh the benefits of air-gapped operation against potential performance bottlenecks for extremely long contexts and the smaller window size relative to some cloud alternatives.

When should I choose a different model over Qwen2.5-Coder 32B (Ollama) for Atlas?

While Qwen2.5-Coder 32B (Ollama) is a strong local contender for Atlas in 2026, you should consider other models if your primary need is a context window significantly larger than 32K tokens. This model's 32,768 token limit can be restrictive for extremely large codebases or complex, multi-file refactoring tasks.

If your development workflow frequently involves very large codebases that demand context windows exceeding 32K tokens, or if you require the absolute highest throughput for long context interactions without any risk of VRAM offload, a cloud-based model might be a more suitable choice for Atlas. Qwen2.5-Coder 32B (Ollama) is optimized for the practical ceiling of a single 24GB consumer GPU, but pushing its 22GB serving requirement with a full 32K context on such a card will inevitably lead to performance degradation. For developers prioritizing raw speed and massive context over air-gapped security and free self-hosting, exploring cloud providers that integrate with Atlas's Model Context Protocol servers would be advisable. Atlas allows switching models on the fly, so you can always use Qwen2.5-Coder 32B (Ollama) for sensitive local work and a cloud model for tasks requiring immense context.

Setup

011: Pull the Qwen2.5-Coder 32B model: Open your terminal and execute "ollama pull qwen2.5-coder:32b". This command downloads the 20GB model weights.
022: Verify VRAM headroom: Before committing, run "atlas device" to check your system's available VRAM. You will need approximately 22GB free, with more recommended if you plan to frequently utilize the full 32K token context window.
033: Configure Atlas for Qwen2.5-Coder 32B: Edit your "atlas.json" file. Under the "ollama" provider, add "qwen2.5-coder:32b" to the "models" map. Set "limit.context" to "32768" and "limit.output" to "8192" for this model.
044: Set active and small models: In "atlas.json", configure your "model" setting to "ollama/qwen2.5-coder:32b". Crucially, set "small_model" to "ollama/qwen2.5-coder:1.5b" to prevent the smaller model slot from evicting the 20GB weights of the 32B model.
055: Enable local embeddings for privacy: To ensure complete air-gapped operation, point Atlas's codebase index at the local Ollama embedder. This keeps all AST chunks and their corresponding vector embeddings entirely offline on your machine.

Frequently asked questions

What hardware do I need to run Qwen2.5-Coder 32B (Ollama) with Atlas?: To run Qwen2.5-Coder 32B (Ollama) effectively with Atlas, you will need a GPU with at least 24GB of VRAM, such as an NVIDIA 3090, 4090, or AMD 7900 XTX, or a Mac with 32GB or more unified memory. The model requires roughly 22GB to serve.
Is Qwen2.5-Coder 32B (Ollama) truly air-gapped when used with Atlas?: Yes, Qwen2.5-Coder 32B (Ollama) is air-gapped by construction. When configured with Atlas and the local Ollama embedder, all model weights, prompts, diffs, and the codebase's vector index remain entirely on your local machine, never leaving your network.
What is the context window size for Qwen2.5-Coder 32B (Ollama) in Atlas?: Qwen2.5-Coder 32B (Ollama) provides a 32K tokens (32,768) context window when used with Atlas. This is a robust size for many local coding tasks.
How does Qwen2.5-Coder 32B (Ollama) compare to smaller Qwen2.5-Coder models for Atlas?: Qwen2.5-Coder 32B (Ollama) is the largest and strongest fully offline coding model in its series that most people can run. It handles Atlas's plan-then-build flows and permission-gated tool chains with significantly fewer derailments compared to the 7B or 14B versions.
Will using a long context with Qwen2.5-Coder 32B (Ollama) impact performance on a 24GB GPU?: Yes, while the model fits on a 24GB card, its roughly 22GB serving requirement means that utilizing a very long context, approaching the 32K token limit, will likely push the system into VRAM offload, which can reduce throughput and increase response times.
Can Atlas switch between Qwen2.5-Coder 32B (Ollama) and other models?: Yes, Atlas is designed to let you switch the active model and provider on the fly. This allows you to use Qwen2.5-Coder 32B (Ollama) for local, air-gapped work and easily switch to other models, including cloud-based ones, for tasks requiring different capabilities or larger contexts.
Is Qwen2.5-Coder 32B (Ollama) free to use with Atlas?: Yes, Qwen2.5-Coder 32B (Ollama) is free (self-hosted), meaning there are no direct costs associated with using the model itself, beyond your own hardware and electricity.

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