While Apple's marketing team crafts charts showing unreachable performance gains, the real world is putting the hardware to the test. A Mac rental service provider ran the M4 Mac mini with 16GB of RAM through Ollama (version 0.31.2). The methodology was transparent: they excluded the "warm-up" run to account for weight loading times and measured generation speeds on popular Q4_K_M quantized models. The results revealed that the 120 GB/s memory bandwidth is the definitive bottleneck dictating the rules of the game. The formula is simple: divide the bandwidth by the model size, and you get the real-world performance rate, free of illusions.
The Entry Ticket with a Hard Ceiling For businesses, the base M4 machine is a budget-friendly entry into the world of local assistants, but it comes with a clear ceiling. Models with up to 8 billion parameters (8B) deliver a solid 20+ tokens per second. That is faster than the human reading speed. This level of performance is sufficient for automated document processing, coding assistants, or local RAG agents handling confidential data without leaking it to the cloud. However, the moment you aim higher—at a 14B model, for example—speed drops to 11.7 tokens per second. This reaches the level of agonizing delay that quickly frustrates employees during constant use.
"Memory bandwidth of 120 GB/s is the primary constraint for inference on base M4 chips"
Technical Bottlenecks and Economics Interestingly, context size has almost no impact on speed: prompt processing is significantly faster than generation itself, reaching 1,720 tokens per second. This means you can "feed" the machine a bulky report instantly, but you will have to wait a long time for a thoughtful response. In our view, deep analytics using 14B+ models will require versions with up to 546 GB/s bandwidth and at least 24GB of RAM.
The Bottom Line The Mac mini remains the most affordable way to deploy autonomous Edge AI in the office and resolve data security concerns. However, Capex must be evaluated realistically: by saving at the start, you risk building a fleet of machines that choke on mid-level intellectual tasks. While it is an ideal solution for simple assistants, a full-scale AI transformation on "minimal" specs will quickly hit the wall of hardware physics.