Your MacBook Is Now a Data Center. No Subscription Required.
Meta's Llama 3 70B now runs at practical speeds on consumer M1 and M2 Macs using Apple's MLX framework. This eliminates cloud dependency for applications like private coding assistants.
This proves that local inference for large models is no longer theoretical. You should evaluate whether your data sensitivity justifies the hardware investment, because every prompt sent to a cloud API is a prompt you do not control.
Meta developed Llama 3 70B; Apple and the MLX open-source community enabled the framework. The GitHub repository for mlx-examples contains the implementation, though specific benchmark numbers for tokens-per-second on each chip variant are not provided in the source.
Step 1: Install Homebrew if absent, then run 'brew install python' and 'pip install mlx-lm' in Terminal. Step 2: Download a quantized Llama 3 variant by running a Python script from the mlx-examples repository at https://github.com/ml-explore/mlx-examples/tree/main/llms. Step 3: Execute the provided inference script with your own prompt and observe local generation without network activity, confirming privacy preservation.