The first Apple M5 Max machines with 128GB of unified memory are landing in the hands of local AI enthusiasts, and early performance testing is underway.
One tester documented their progression from a Raspberry Pi with a Hailo 10H accelerator to a MacBook M3 Pro with 16GB, where they "immediately saw the potential" for running models locally through Ollama. The M5 Max with 128GB represents a massive jump - eight times the memory of that M3 Pro, all available as unified memory that both the CPU and GPU can access without the bottleneck of copying data between separate pools.
That 128GB matters because it determines the size of AI model you can load. A 16GB machine limits you to smaller models or heavily compressed versions of bigger ones. With 128GB, you can run 70-billion-parameter models (roughly GPT-3.5 scale) at reasonable quality settings, or even load quantized versions of larger models that would otherwise require a multi-GPU server setup costing several thousand dollars.
Apple Silicon's memory bandwidth - the speed at which data moves to and from memory - has been the key advantage for local inference (the process of actually running a model to generate text). Each generation has improved this, and the M5 Max is expected to push it further, directly translating to faster token generation speeds.
The local LLM scene has grown fast over the past year. Tools like Ollama and llama.cpp have made it straightforward to download and run open-weight models from Meta, Mistral, and others on consumer hardware. Apple's high-memory laptops have become the default recommendation for anyone who wants capable local AI without building a desktop with an expensive NVIDIA GPU. A maxed-out M5 Max isn't cheap, but it's a single purchase that runs silently on battery power - a different value proposition than renting cloud GPU time at $2-4 per hour.