New empirical data on the performance of language models based on Apple Silicon chips has been published. Testing on the M4 Mac mini with 120 GB/s memory bandwidth showed high efficiency for running compact models via Ollama.
What Happened
LLM generation speed measurements were conducted on a Mac mini with an M4 chip (16 GB RAM, 120 GB/s bandwidth) using Ollama 0.31.2. For Llama 3.2 3B, a speed of 46.7 tok/s was recorded; for Mistral 7B, 22.8 tok/s; and for DeepSeek R1 8B, 20 tok/s. The results are based on the median value of three runs with a fixed prompt and a 512-token budget.
Context
Neural network performance on the Apple Silicon architecture demonstrates a direct correlation with unified memory bandwidth rather than the computational power of the chip's cores. Using Ollama provides a standardized environment for conducting such local tests.
Why It Matters for the Industry
The data confirms that high memory bandwidth (120 GB/s) makes local inference of models up to 8B parameters commercially viable for creating autonomous edge agents and private AI infrastructures. This allows engineers to more accurately plan hardware stacks when deploying local RAG systems.
Why It Matters for Users
Users can use these metrics to assess the suitability of the M4 Mac mini for running specific models (Llama, Mistral, Qwen) in local mode. This enables rapid prototyping of private productivity tools and personal AI assistants that do not depend on cloud APIs.
What Is Not Yet Known / Limitations
The results are specific to the particular memory configuration (120 GB/s) and Ollama version 0.31.2. There is also a difference in data interpretation: ranging from purely technical confirmation of correlation to assessing the product applicability of edge solutions.
Sources
Author
Look at AI, Editorial Staff