Local AI exploration in 2026 has been dominated by Nvidia's GB10 in the DGX Spark and AMD's Ryzen AI Max+ 395 in systems like the Corsair AI Workstation 300. But Apple's Mac Studio with the M4 Max is proving to be a powerful contender, thanks to a unified memory architecture that offers 128GB at 546GB/s. We tested the 16-core CPU variant with a 40-core GPU to see if that memory bandwidth delivers real-world wins in local LLM inference.
The M4 Max configuration we tested
Apple supplied us with an M4 Max Mac Studio featuring a 16-core CPU (12 performance cores and four efficiency cores), a 40-core GPU, and 128GB of LPDDR5X-8533 memory. At launch it cost around $3,699, close to the $3,999 DGX Spark. The RAMpocalypse has since taken its toll, and this exact configuration is no longer available, the current maximum is 64GB, with lead times exceeding two months. Still, it remains the only platform in this class with a 512-bit memory bus.
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Why memory bandwidth drives decode performance
LLM inference is heavily sequential during the decode phase. Each generated token must pass through every layer of the model, requiring all weights from that layer to be streamed into the GPU. Since the compute per token is tiny, throughput becomes dependent on how quickly those weights can be read. A large memory pool holds larger models, and a wide bus speeds up weight streaming. That is exactly what the M4 Max offers, with double the bandwidth of GB10 and Strix Halo.
Benchmark results with llama.cpp and three models
We used llama.cpp built with native Metal support and the llama-benchy harness. The models tested were Qwen 3.6-35B-A3B, a mixture-of-experts model, Gemma 4 12B, a small dense model, and OpenAI's gpt-oss-120b, all in 4-bit quantization from Unsloth. In prompt processing, the Mac Studio outperforms Strix Halo but lags GB10 on longer contexts. In decode throughput, however, the M4 Max leads both systems at every context depth, delivering higher tokens-per-second.
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Memory bandwidth is not the whole story
Despite having twice the theoretical bandwidth, the actual gain on Qwen 3.6-35B-A3B was just 25% over the competition. That welcome improvement is far from a doubling. With the dense Gemma 4 12B, though, the bandwidth advantage becomes more pronounced, with throughput scaling almost linearly. This shows that model architecture and kernel optimization are equally important. Raw bandwidth doesn't guarantee proportional performance.
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Security concerns remain for local LLMs
Beyond performance, adopting local LLMs in production raises serious security questions. A study presented at ICML 2026 demonstrated that LLMs cannot be made fully secure, a reality that businesses must face alongside their infrastructure choices. Nevertheless, the Mac Studio M4 Max is a compelling option for developers prioritizing high decode throughput in a compact desktop. For a comprehensive overview of the hardware, the Wikipedia page on Mac Studio is a solid reference.
