Apple Silicon inference optimizations differ greatly from server GPUs
Evaluating the Applicability of LLM Inference Optimizations on Apple Silicon
Machine LearningDistributed, Parallel, and Cluster Computing
Summary
This paper looks at how popular tricks to speed up AI language models work on Apple Silicon computers compared to powerful NVIDIA GPUs usually used in data centers. The authors found that some tricks make Apple Silicon faster, while others that help on GPUs actually slow it down. They tested four different types of tricks and saw that the results depend on the hardware, the software, and how the tasks are run. So, just because an optimization works on expensive servers, it doesn’t mean it will work the same on everyday Apple devices.
What this means in practice
- •For mobile app developers: Optimize local AI language model performance by selecting Apple Silicon-specific inference techniques for single user requests.
- •For hardware engineers: Design next-generation Apple processors with better support for advanced AI inference features like asynchronous copy and batch decoding.
Authors
Kyrylo Yemets, Andrii Yaroshevych, Victor Muryn, Ostap Khomenko, Pavlo Kryven, Maksym Kmet, Maksym Shamrai, Mariya Hirna
Abstract
Apple Silicon is among the most widely used platforms for local LLM inference, yet nearly all inference optimizations are designed and validated on server-class NVIDIA GPUs under batched serving workloads. Which optimizations retain their benefits when moved to Apple Silicon, and what determines their applicability? We evaluate four optimization families: KV-cache pruning, speculative decoding, ported GPU kernels, and post-training quantization. Experiments span four Apple machines under a common single-request decode workload, with matched RTX 4090 comparisons where possible. KV-cache pruning can reach up to 1.75 times end-to-end throughput on Apple but can reduce throughput on desktop NVIDIA GPUs. Under the common benchmark, all evaluated speculative decoding methods reduce throughput on Apple, despite reported 2-6 times gains on server GPUs. CUDA kernel designs transfer only partially: public Metal lacks asynchronous copy, and batch-one decode makes several serving-oriented mechanisms inapplicable, although transferable components yield up to 1.20 times decode throughput. Quantization gains vary by machine. Across four families, a reported speedup is not a portable property of a technique alone but a joint function of technique, runtime, hardware contract, and workload regime.