Apple M4 Max Outpaces Nvidia and AMD in Local AI Decode Throughput

Apple's M4 Max chip has established a significant lead in local AI performance, outperforming both Nvidia and AMD in critical large language model (LLM) metrics. Recent testing reveals that the Mac Studio's architecture provides a distinct advantage for developers running AI models locally.

According to tests conducted by Tom's Hardware, the M4 Max achieved higher tokens-per-second throughput than Nvidia's GB10 (DGX Spark) and AMD's Ryzen AI Max+ 395 (Strix Halo). This performance lead in LLM decode throughput remained consistent across all tested context depths, positioning the M4 Max as the faster option for generating text output in local environments.

The Bandwidth Bottleneck

To understand these results, it is necessary to distinguish between the two phases of LLM inference: prefill and decode. While the prefill phase handles the initial input, the decode phase is sequential, requiring every output token to be processed through every layer of the model. Because of this sequential nature, the process depends heavily on memory bandwidth—the speed at which model weights stream from memory to the GPU—rather than raw compute power or TFLOPS.

Apple's hardware is specifically optimized for this bottleneck. The M4 Max features 546 GB/s of memory bandwidth. This is significantly higher than the competition, roughly doubling the 273 GB/s found in the Nvidia GB10 and the 256 GB/s provided by the AMD Strix Halo.

Industry Implications

For AI researchers and developers, these findings highlight a shift in what constitutes "performance" for local AI. While Nvidia and AMD typically lead in raw compute capabilities, Apple's unified memory architecture creates a more efficient path for high-throughput token generation. The ability to combine a large unified memory pool with extreme bandwidth allows the Mac Studio to handle larger models with greater fluidity than traditional discrete GPU setups.

Future Outlook

As local AI adoption grows, the battle between raw TFLOPS and memory bandwidth will likely intensify. While the M4 Max currently holds the edge in decode speed, the industry will be watching to see if Nvidia and AMD can narrow the bandwidth gap in future iterations of their AI-focused silicon. For now, the M4 Max remains the benchmark for local LLM throughput, provided users have the budget for Apple's high-end hardware.