Memory Allocation Settings for Running oMLX on a 16GB MacBook Without Freezing

Apple Silicon Macs feature Unified Memory where the CPU and GPU share the same RAM. This is why running local LLMs without proper configuration can cause the entire system to freeze. On 16GB models specifically, if an LLM occupies all available resources, applications like VS Code or web browsers will start to lag. To use oMLX as a practical development tool rather than just a toy, you must first give the OS some room to breathe.

Memory Limit Settings to Prevent System Freezing

You should never allow local LLM processes to consume RAM indefinitely. The macOS kernel and IDE language servers require a minimum amount of free space to operate. When running oMLX, you must use the max-process-memory flag to forcibly set an upper limit.

• Method: Append the --max-process-memory 0.65 option when launching oMLX from the terminal. For a 16GB model, this setting reserves approximately 5.6GB for the system. If you are on an 8GB model, you should lower this value to 0.5 and stick to models under 3B parameters.
• Result: Even during model inference, VS Code input latency remains under 200ms. This prevents the system from locking up and keeps the memory pressure graph in Activity Monitor from turning red.

API Connection via Continue Extension

Using oMLX only in the terminal limits its potential. You should integrate it into your actual coding workflow by connecting it to Continue, a VS Code extension. The key here is not to rely on a single heavy model for everything, but to separate models based on their purpose.

• Method: In the Continue config.json, set the provider to openai and the apiBase to http://localhost:8000/v1. While you might use a 7B~9B model for chat, assign a lightweight model like qwen2.5-coder-1.5b-mlx to the tabAutocompleteModel field.
• Result: You can experience fast code autocompletion at a 10ms level while saving the $20 monthly subscription fee.

Dedicated Volume Allocation for SSD Longevity

When memory is insufficient, oMLX offloads the KV cache to the SSD. If this process repeats on the system root volume, it increases I/O load and can negatively impact SSD lifespan in the long run. It is wiser to physically isolate the AI workspace using the APFS container feature.

• Method: Add an APFS volume named AI_Storage in Disk Utility. Secure space by setting a reserved size of 20GB, then lock the path by using the --paged-ssd-cache-dir /Volumes/AI_Storage/cache option when running oMLX.
• Result: This reduces I/O bottlenecks that occur during large-scale project analysis. It also prevents fragmentation of the system drive, protecting the overall responsiveness of your MacBook.

Building an Independent Environment Using uv

MLX-based tools often suffer from Python dependency conflicts. Installing various packages via pip can easily break your existing project environments. Using uv, a package manager written in Rust, solves this issue cleanly.

• Method: Install uv using curl -LsSf [https://astral.sh/uv/install.sh](https://astral.sh/uv/install.sh) | sh, then create an independent environment with uv venv --python 3.12. Afterward, install all necessary libraries at once by entering uv pip install omlx[mcp].
• Result: Reduces environment setup time to about one minute. If you need to update models or if packages get tangled later, management is easy—just delete the virtual environment folder.

oMLX is more power-efficient and faster at generation than llama.cpp, but it will monopolize system resources if left unchecked. Simply yielding 40% of your RAM to the OS and isolating SSD I/O is enough to create a comfortable local AI development environment. Practical settings that keep your MacBook stable are far more important than raw benchmarks.