Real Mac performance for long local AI workloads
The focus is real daily performance on Macs with 64GB or more unified memory when running . The main workload is feeding the model a lot of context, such as long chat histories, full documents, codebases, or large setups. The key questions are how fast 7B to 70B models read long input and how fast they generate replies.
The setup details matter, including quantization and tools such as MLX, llama.cpp with Metal, Ollama, and LM Studio. is a major concern because performance may change at 32k, 64k, or 128k and beyond. Heat also matters: long sessions may warm the machine, add room heat, or cause throttling, especially when comparing MacBooks with Mac mini or Mac Studio machines.
The practical question is whether M3 and M4 Pro, Max, or Ultra Macs can handle hours of use or continuous server-style operation without becoming slow or uncomfortable.
Key points
- The item focuses on Macs with 64GB or more unified memory running .
- The main concern is heavy context, such as long chats, full documents, codebases, and .
- It asks for separate real-world speeds for reading long input and generating replies.
- It compares tools such as MLX, llama.cpp with Metal, Ollama, and LM Studio.
- It treats heat, throttling, and continuous server-style use as practical deal-breakers.