What is the context window size for Code Llama 34B (Ollama) in Atlas?

Code Llama 34B (Ollama) provides a 16K tokens (16,384) context window when used with Atlas. Atlas uses its advanced retrieval capabilities to ensure this window is efficiently filled with relevant code chunks.

How much does it cost to run Code Llama 34B (Ollama) with Atlas?

Code Llama 34B (Ollama) is Free (self-hosted) to run with Atlas. This means you incur no token costs, allowing for unlimited retries, re-plans, and regenerations within Atlas.

What hardware is needed to run Code Llama 34B (Ollama) locally?

To run Code Llama 34B (Ollama) locally, you will need approximately 21GB of free VRAM or unified memory to serve its 19GB Q4_K_M weights. You can verify your system's capacity using the `atlas device` command.

Is Code Llama 34B (Ollama) good for generating entire code files?

Yes, Code Llama 34B (Ollama) is materially better at whole-function and whole-file generation compared to its 7B counterpart. Atlas further enhances this by feeding it relevant code chunks via AST declarations and local embeddings.

Can Atlas use local embeddings with Code Llama 34B (Ollama)?

Yes, Atlas can build its code index with local Ollama embeddings, keeping your code off third-party servers. This is crucial for optimizing the 16K context window of Code Llama 34B (Ollama) by providing highly relevant code segments.

Why is the 34B version recommended over the 70B version for Ollama?

The 70B tag on Ollama shows a 2K context, which is almost certainly bad metadata and not a usable specification. The 34B version is the last size in this line that behaves sanely and offers a practical 16K context window.

How does Atlas handle the 16K context window limitation of Code Llama 34B (Ollama)?

Atlas addresses the 16K context window by leveraging its AST chunking (tree-sitter) and local Ollama embeddings. This allows Atlas to feed the model highly relevant code snippets, maximizing the utility of the available context for focused tasks rather than processing entire files.

Atlas with Code Llama 34B (Ollama) in 2026

Code Llama 34B (Ollama) is an excellent choice for developers in 2026 seeking a powerful, self-hosted coding agent model that costs nothing to run. It excels at generating complete functions and files within Atlas, leveraging its 16K token context window for focused code tasks and enabling unlimited retries and re-plans.

What is Code Llama 34B (Ollama) best for with Atlas?

Code Llama 34B (Ollama) is best suited for developers in 2026 who need robust whole-function and whole-file code generation within Atlas. This 19GB model significantly outperforms its 7B counterpart in these specific tasks, offering a consistent 16K token context window across its smaller siblings.

This model's strength lies in its ability to generate more complete and coherent code blocks, a direct benefit of its larger parameter count compared to the 7B and 13B versions. When integrated with Atlas, Code Llama 34B (Ollama) can leverage Atlas's advanced code indexing by AST declarations using tree-sitter and hybrid semantic and keyword retrieval (Axis) to focus its 16K token context window on the most relevant code snippets. This synergy allows Atlas to feed the model precise chunks of code, enabling it to draft plans in a read-only plan agent and then generate high-quality code edits, which are then presented as unified diffs for approval. The model's local nature means Atlas can retry, re-plan, and regenerate diffs without incurring any token costs, making it ideal for iterative development workflows where a developer can switch the active model and provider on the fly.

What are the cost and context window tradeoffs for Code Llama 34B (Ollama)?

Code Llama 34B (Ollama) offers a compelling value proposition in 2026: it is entirely Free (self-hosted) to run, allowing unlimited usage without a token meter. However, its 16K token context window is considered small for an agent, requiring strategic use of Atlas's retrieval capabilities.

The primary advantage of Code Llama 34B (Ollama) is its zero operational cost, as it runs locally on your hardware. This enables Atlas to perform extensive operations like re-planning, retrying tool calls, and regenerating code diffs without any financial penalty. The model requires approximately 21GB of VRAM or unified memory to serve its 19GB Q4_K_M weights. While its 16K tokens (16,384) context window is generous for many tasks, it is relatively small for an AI agent that might need to process entire large files. To mitigate this, Atlas is designed to lean heavily on its AST chunking (tree-sitter) and local Ollama embeddings to ensure that the 16K window is filled with the most relevant code segments rather than entire files. This approach maximizes the utility of the available context, making the model effective despite its size limitation for agentic workflows.

When should I choose a different model over Code Llama 34B (Ollama)?

While Code Llama 34B (Ollama) is a strong contender for local AI coding in 2026, developers should consider other models if they lack 21GB of available VRAM or require a significantly larger context window for their Atlas workflows. The 34B version is the practical top of this line.

You should consider an alternative model if your hardware cannot reliably serve the 19GB Q4_K_M weights, which typically requires around 21GB of VRAM or unified memory. Additionally, if your primary use case involves processing extremely large codebases or files that consistently exceed the effective capacity of a 16K token context window, even with Atlas's advanced retrieval mechanisms, a model with a much larger native context might be more efficient. While Atlas's AST chunking and local Ollama embeddings help manage the 16K window effectively by feeding relevant code, there are limits to what can be achieved without a larger inherent context. Furthermore, if you prioritize convenience over self-hosting and are willing to pay for API access, cloud-hosted models might offer simpler setup and potentially larger context windows, though at a recurring cost. The 70B tag on Ollama is noted to have a 2K context, which is not a usable specification, making the 34B the largest practical and sane option in this specific Code Llama series for local deployment.

Setup

01Pull the large size of the model using the command: `ollama pull codellama:34b` (this is a 19GB download).
02Verify roughly 21GB of free VRAM or unified memory on your system using the `atlas device` command.
03In your `atlas.json` configuration file, register `codellama:34b` under the `ollama` provider.
04Configure the context and output limits for this model in `atlas.json`: set `limit.context` to `16384` and `limit.output` to `4096`.
05Set your primary `model` to `ollama/codellama:34b` and your `small_model` to a 3B tag to prevent the 19GB weights from being evicted during summarization tasks.
06Ensure Atlas is configured to lean heavily on its AST chunking (tree-sitter) and local Ollama embeddings to optimize the 16K window for relevant code.

Frequently asked questions

What is the context window size for Code Llama 34B (Ollama) in Atlas?: Code Llama 34B (Ollama) provides a 16K tokens (16,384) context window when used with Atlas. Atlas uses its advanced retrieval capabilities to ensure this window is efficiently filled with relevant code chunks.
How much does it cost to run Code Llama 34B (Ollama) with Atlas?: Code Llama 34B (Ollama) is Free (self-hosted) to run with Atlas. This means you incur no token costs, allowing for unlimited retries, re-plans, and regenerations within Atlas.
What hardware is needed to run Code Llama 34B (Ollama) locally?: To run Code Llama 34B (Ollama) locally, you will need approximately 21GB of free VRAM or unified memory to serve its 19GB Q4_K_M weights. You can verify your system's capacity using the `atlas device` command.
Is Code Llama 34B (Ollama) good for generating entire code files?: Yes, Code Llama 34B (Ollama) is materially better at whole-function and whole-file generation compared to its 7B counterpart. Atlas further enhances this by feeding it relevant code chunks via AST declarations and local embeddings.
Can Atlas use local embeddings with Code Llama 34B (Ollama)?: Yes, Atlas can build its code index with local Ollama embeddings, keeping your code off third-party servers. This is crucial for optimizing the 16K context window of Code Llama 34B (Ollama) by providing highly relevant code segments.
Why is the 34B version recommended over the 70B version for Ollama?: The 70B tag on Ollama shows a 2K context, which is almost certainly bad metadata and not a usable specification. The 34B version is the last size in this line that behaves sanely and offers a practical 16K context window.
How does Atlas handle the 16K context window limitation of Code Llama 34B (Ollama)?: Atlas addresses the 16K context window by leveraging its AST chunking (tree-sitter) and local Ollama embeddings. This allows Atlas to feed the model highly relevant code snippets, maximizing the utility of the available context for focused tasks rather than processing entire files.

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