How Many Tokens Per Second Can a Mac Hit on a 70B Model?

The honest answer to how fast a Mac runs a big local model is not a single number, it is a usable band, and for a 70B model on Apple Silicon that band lands squarely in interactive territory. An M5 Max chews through a 70-billion-parameter model fast enough that the text appears at conversational pace rather than crawling out word by word, which is the threshold that actually matters when you are coding against it.

Quick Answer

An M5 Max generates roughly 20 to 28 tokens per second on a 70B model quantised to Q4, fast enough for comfortable interactive use. The driver is its unified memory bandwidth, which sits in the 546 to 614 GB/s range and moves the model weights quickly enough to keep generation flowing. Exact numbers shift with the framework and memory configuration, but that band is what to expect.

Why bandwidth sets the speed

Token generation on a large model is bound by memory bandwidth more than raw compute, because every token requires reading the entire set of model weights from memory. The faster the chip can pull those weights, the more tokens per second you get. The M5 Max moves data in the region of 546 to 614 GB/s across its unified memory, and that figure translates almost directly into its throughput on big models.

This is why the M5 Max can hold a usable pace on a 70B model where a chip with narrower memory would stall. The model is large, the weights are heavy, and the wide memory pipe is what keeps them flowing fast enough to feel responsive.

What the numbers actually look like

Reported figures cluster around 20 to 28 tokens per second for a 70B Q4 model on an M5 Max, with some setups landing a little lower and well-tuned ones a little higher. The spread comes from real variables: the inference framework you use, your memory configuration, and the exact quantisation of the model. A 128GB machine running an optimised framework sits at the top of that band, while a smaller-memory setup or a less efficient runtime sits lower.

For context, that pace is comfortably faster than most people read. Generation flows smoothly enough for back-and-forth coding work, where you are reading and reacting rather than waiting for a wall of text. It is not the raw speed a top-end discrete graphics card hits on a model small enough to fit its memory, but for a 70B model that overflows most single cards, the M5 Max delivering interactive speed at all is the point. You can compare the AI-focused systems Evetech builds for local model work if you are weighing a PC against this, and the machines local buyers are choosing most show where the value currently sits.

Getting the most from it

To reach the upper end of that band, two things help most: enough unified memory to hold the model plus context without spilling, and an efficient inference framework tuned for Apple Silicon. A 128GB machine gives comfortable headroom for a 70B model and a generous context window, and a well-optimised runtime extracts more tokens per second from the same hardware than a generic one. Get both right and you sit at the top of the range rather than the bottom.

Frequently Asked Questions

How many tokens per second does an M5 Max do on a 70B model?

Roughly 20 to 28 tokens per second on a 70B Q4 model, fast enough for interactive coding use. The exact figure depends on your framework, memory size, and quantisation, with optimised 128GB setups landing at the higher end of that range.

Why is memory bandwidth the limiting factor?

Generating each token requires reading the full set of model weights from memory, so the speed at which the chip can pull those weights caps throughput. The M5 Max's 546 to 614 GB/s bandwidth is what lets it keep a 70B model flowing at usable speed.

Is 20 to 28 tokens per second fast enough to work with?

Yes, comfortably. That pace is faster than most people read, so generation flows smoothly for coding and chat. You are reading and reacting rather than waiting, which is exactly the interactive experience you want from a local model.

What affects whether I hit the top of that range?

Memory capacity and your inference framework. A 128GB machine holds the model and context without spilling, and a runtime optimised for Apple Silicon extracts more tokens per second from the same chip than a generic one. Both together put you at the top of the band.