Apple Silicon speedups depend on the workload and runtime

Local inference on Apple Silicon runs under a different hardware, software and single-request setup from server GPUs where many speedups were measured. It remains unclear whether those optimizations still help in that setting, or whether their assumptions stop matching the work the device must do.

The authors compare four inference-optimization families on Apple Silicon to show that gains depend on how each method fits the runtime, hardware, and workload.

The authors test four families by isolating individual methods, porting representative GPU mechanisms to Metal and MLX, and measuring kernel and end-to-end effects across four Apple devices. They include matched RTX 4090 comparisons where configurations align.

Results vary by method and workload: pruning can cut Qwen3-4B latency in long-prompt runs, but speculative decoding added no full-response rate gain in the principal local-chat comparison.

Kernel and quantization changes also produced bounded gains, supporting the authors’ conclusion that portability depends on the technique’s fit with runtime, hardware and workload.

These are performance measurements for the tested local workloads. The pruning experiments did not assess output quality, the megakernel case covered one model on one Apple chip, and multi-request serving was left untested.