Running a local model is straightforward. Making it useful inside a coding workflow is the harder part. An agent must inspect files, run tools, read the output, and decide what to do next. The model is only one part of that loop.
MLX provides a practical local stack for Apple silicon: MLX handles array computation, MLX-LM loads and runs language models, and MLX-LM Server exposes a local HTTP endpoint for an agent client. The important design choice is to keep the model endpoint local while being precise about which tools still use the network.
Start with a compatible local endpoint
MLX-LM Server exposes an API compatible with the OpenAI Chat Completions shape. That lets an agent client that supports this protocol use a local base URL instead of a cloud provider. Start with a small tool-capable model to validate the full loop before choosing a larger coding model.
pip install mlx-lm
mlx_lm.server --model mlx-community/Qwen-3.5-4B-8bit
curl http://127.0.0.1:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"default_model","messages":[{"role":"user","content":"Hello!"}]}'The curl request is a boundary check. It verifies that the model is loaded and that the client can reach the server before configuration is added to an agent tool. Then set the agent's base URL to http://127.0.0.1:8080/v1 and use the model name expected by the server.
An OpenAI-compatible endpoint does not make every client configuration interchangeable. Confirm the protocol your agent expects, the model's tool-calling support, and the exact endpoint it uses. The MLX-LM server documentation also describes it as a local HTTP server with basic security checks, not a public production service. Keep it bound to localhost unless you have designed the authentication and network controls yourself.
Local inference does not make every tool call local
The model can stay on the Mac while an agent's tools do other work. A command that reads a repository is local. A git command that fetches pull requests, a web search, or an API call is not. That distinction matters for privacy claims and for debugging a slow workflow.
Give the agent the smallest useful tool set. Let it read project files, run focused builds, and inspect diffs. Require review before it changes important files or runs commands outside the project. A local endpoint reduces where prompts and source code go, but it does not replace normal permissions or code review.
Optimize the context loop first
Agent workloads repeatedly send code, build logs, diffs, and tool results back to the model. That makes prompt processing a first-order performance problem, often more important than raw generated tokens.
The first fixes are architectural rather than hardware-specific:
- keep tool output focused instead of pasting whole repositories or unfiltered logs;
- ask subagents for short findings rather than full transcripts;
- bound build output and pass only the failing diagnostic and nearby code;
- reset or summarize a session when its history no longer helps the next decision.
MLX-LM Server can use continuous batching when several requests arrive together, which is useful for concurrent subagents. It does not remove the need to control context size. Better inputs make every local model more responsive.
Scale only when one Mac is the bottleneck
Distributed inference is useful when the chosen model cannot fit on one Mac or when prompt processing needs more throughput. MLX can launch a server across multiple machines and shard the model, but that adds host configuration, transport, and operational complexity.
Treat a single Mac, a local endpoint, and one reliable agent workflow as the first milestone. Measure model load time, time to first response, prompt-processing time, and the success rate of tool calls. Add a larger model or more machines only after those measurements point to a real constraint.
The appealing part of this stack is not merely offline chat. It is the ability to run a review, build, or repair loop against local model weights while keeping control over the endpoint, the context, and the tools it may invoke.