Mac Mini M4 Local LLM Benchmarks: Real Tokens/Sec

The Hardware and the Baseline Setup

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You bought the new Mac mini M4, booted it up, and ran ollama run llama3.1. It works, but you are getting weird stutters, or you are wondering if you are actually getting the maximum tokens per second this silicon can push. Or maybe you are staring at the Apple Store checkout page right now, trying to figure out if the $599 base 16GB model is a toy or a legitimate local AI server.

This post is for you. I bought the base Mac mini M4 (10-core CPU, 10-core GPU, 16GB unified memory, 256GB SSD) specifically to test its limits as a headless LLM node.

Apple Silicon changes the game for local AI because of unified memory. Unlike a PC where you have to copy model weights from system RAM to a dedicated GPU's VRAM, the M4 chip shares its memory pool. The GPU cores can read the weights directly. The bottleneck on these machines is rarely compute; it is almost entirely memory bandwidth. The base M4 chip has a memory bandwidth of 120 GB/s. We will see exactly how that translates to text generation speed.

I started with a clean install of macOS Sequoia. I did not install Xcode, just the command line tools, which you will need for compiling some Python packages later.

xcode-select --install

Installing Ollama and Monitoring the Silicon

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Ollama is the easiest way to get an LLM running locally. It wraps llama.cpp and handles model quantization and execution. I prefer installing it via Homebrew rather than downloading the Mac app because it makes it easier to run as a background daemon on a headless machine.

brew install ollama
brew services start ollama

Before we run a model, we need a way to actually see what the hardware is doing. The built-in Activity Monitor is terrible for tracking Apple's GPU and Neural Engine. Instead, I use asitop, a command-line tool that reads the built-in macOS performance counters.

pip3 install asitop
sudo asitop

Leave this running in a second terminal window. When you load a model, you will see exactly how much unified memory is consumed. This is critical because macOS will actively try to prevent you from using all 16GB for the GPU. If you push it too far, the system starts swapping memory to the SSD, and your tokens/sec will instantly drop from 35 t/s to 2 t/s. Figuring out how much RAM you actually need for local AI is mostly an exercise in keeping your active model weights strictly within the physical hardware limits minus macOS overhead.

Real Tokens/Sec Benchmarks

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I ran a series of popular models using Ollama. To get accurate numbers, you need to use the --verbose flag. This outputs the exact prompt evaluation rate (reading your input) and the evaluation rate (generating the response).

ollama run llama3.1:8b --verbose

When you type a prompt like "Write a Python script to scrape a website," Ollama streams the text, and prints a block of stats at the end. The stat we care about most is eval rate.

Here are the numbers I recorded on the base Mac mini M4 running macOS 15.1, using Ollama version 0.4.1. All models are using standard 4-bit quantization (Q4_0 or Q4_K_M).

The math checks out perfectly. The base M4 has 120 GB/s of bandwidth. The Llama 3.1 8B Q4 model is roughly 4.7 GB. If the GPU has to read the entire model into memory for every single token generated, the theoretical maximum speed is 120 divided by 4.7, which equals 25.5 tokens per second. Because Apple's caching is aggressive and Q4 uses some slightly smaller block sizes in practice, we hit about 34 tokens per second.

This means Llama 3.1 8B runs faster than you can read. Mistral Nemo 12B is very comfortable for conversational AI, and even the 14B Qwen model feels perfectly responsive.

Squeezing More Performance with Apple's MLX

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Ollama is great, but it relies on llama.cpp, which is optimized for broad compatibility. If you want the absolute highest performance on a Mac, you should use Apple's native machine learning framework: MLX.

MLX is designed specifically for Apple Silicon. I often see a 10% to 15% bump in generation speeds when I switch from Ollama to MLX.

Here is exactly how I set it up on the Mac mini. You will need Python installed.

mkdir mlx-test
cd mlx-test
python3 -m venv .venv
source .venv/bin/activate
pip install mlx-lm

Once installed, you can write a simple Python script to load Llama 3.1 8B directly from Hugging Face and benchmark the generation. Create a file named benchmark.py:

from mlx_lm import load, generate
import time

model, tokenizer = load("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit")

prompt = "Explain the history of the internet in 500 words." messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

start_time = time.time() response = generate(model, tokenizer, prompt=text, verbose=True, max_tokens=500) end_time = time.time()

print(f"\nTotal time: {end_time - start_time:.2f} seconds")

Run this with python benchmark.py. When I ran this on the M4, the generation speed hit 39.1 tokens/sec. That is a measurable jump over Ollama's 34.2 tokens/sec. If you are building a dedicated application, relying on the mlx-lm library natively in Python is the way to go.

Adding a UI and Fixing Connection Issues

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Running models in the terminal gets old quickly. I run Open WebUI via Docker on my Mac mini so I can access it from my Macbook or iPad anywhere in the house.

Install Docker Desktop for Mac, then run this command to pull and start Open WebUI:

docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main

You will likely hit a wall immediately. You open http://localhost:3000, try to select a model, and get a network error. If you find your Open WebUI in Docker cannot reach Ollama, it is because Ollama binds strictly to 127.0.0.1 by default on macOS. Docker containers have their own localhost and cannot see the host machine's Ollama service.

You have to tell Ollama to accept connections from the network. Stop the Ollama service, and set the host environment variable before restarting it.

brew services stop ollama
launchctl setenv OLLAMA_HOST "0.0.0.0"
brew services start ollama

Now, inside Open WebUI's settings, set the Ollama connection URL to http://host.docker.internal:11434. It will connect instantly.

What Did NOT Work (The Dead Ends)

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Benchmarking hardware means finding out where it breaks. I hit a few dead ends with the 16GB M4 mini.

Trying to run a 70B model. I knew it was a long shot, but I tried loading Llama 3 70B quantized to Q2. It requires about 26GB of RAM. I thought macOS swap memory might save me. It did not. The terminal hung for three minutes, asitop showed swap usage skyrocketing, and then the process was forcefully killed by the OS with an error: failed to create context with model output. The 16GB hard limit is real.

Moving models to an external SSD. Because the base mini only has 256GB of storage, I plugged in a USB-C NVMe enclosure and set OLLAMA_MODELS=/Volumes/ExternalDrive/models. It worked, but model load times went from 2 seconds to 15 seconds. Worse, during generation, the eval rate randomly tanked. The USB controller on the Mac mini seems to interrupt the memory bandwidth if the system decides to page out memory while you are reading from the external drive. Keep your active models on the internal SSD.

Applying PC-specific fixes. When debugging low token rates, you will find a ton of advice online about CUDA cores and WSL2. I wasted an hour reading about why Ollama not using my NVIDIA GPU in WSL2 causes slow generation, trying to see if there was an equivalent "force GPU" flag for Mac. There isn't. On Apple Silicon, Ollama uses the Metal API by default. If your generation is slow, you are either out of RAM and swapping, or you are running a model with too many parameters for the 120 GB/s bandwidth.

Is the Base M4 Mac Mini Worth It?

If you are a solo developer or hobbyist, the $599 16GB Mac mini M4 is arguably the best value in local AI hardware right now. It reliably pushes 30+ tokens per second on 8B class models, which is perfect for building a local RAG chatbot or running a coding assistant in VS Code via Continue.dev.

It runs completely silent, draws less than 15 watts of power under full AI load, and fits on a bookshelf. Just do not expect it to run 70B models. For that, you need the Mac Studio or a heavy PC rig with multiple GPUs.

FAQ

Can I run a 70B model on the 16GB M4 Mac Mini?

No, a 70B model at Q4 precision requires about 40GB of RAM just to load the weights. The 16GB Mac mini will instantly kill the process with an Out of Memory (OOM) error before it even starts.

Does the M4 Pro chip make a big difference?

Yes. The base M4 has 120 GB/s of memory bandwidth, while the M4 Pro has 273 GB/s. Since LLM generation is entirely memory-bandwidth bound, the Pro chip will generate text more than twice as fast.

Where does Ollama store the model weights on a Mac?

By default, Ollama on macOS stores downloaded model blobs and manifests in ~/.ollama/models. You can change this location by setting the OLLAMA_MODELS environment variable in your terminal.

Why is my prompt processing speed so much faster than generation?

Prompt processing happens in parallel, calculating multiple tokens at once across the GPU cores. Generation is sequential; the model must output one token before it can predict the next.