mlsanityData to 14 Oct 2026

How Weight Encoding Affects Language Model Placement and Performance on the Apple Neural Engine

arXiv 2608.22110 ↗First posted 22 Aug 2026cs.LG cs.AR cs.PFcited by 0

On the map: Efficient LLM inference and serving → Serving systems and request scheduling

Abstract (arXiv)

Weight compression can alter accelerator placement as well as memory traffic, complicating the interpretation of inference speedups. We investigate this interaction on the Apple Neural Engine through the public Core ML deployment path. Five independently trained language-model checkpoints span two architectures and dense fp16, int8, and ternary weights encoded with two-bit lookup tables. We combine compiler device plans, synchronized memory-controller measurements, and compute-unit exclusion controls for a fixed single-token forward workload. On an M1, the smaller fp16 export executes on the CPU despite permitting ANE execution, whereas its compressed counterparts exhibit ANE activity. The int8 export reduces warm forward latency by a factor of 1.9. The larger fp16 export also uses the ANE, indicating that dense encoding alone does not determine placement. Separate M3 energy measurements support the same direction of change. These results establish encoding-dependent placement in the measured deployment stack and show that compression comparisons require joint measurement of backend selection, latency, and traffic.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Serving systems and request scheduling, one of 12 directions in Efficient LLM inference and serving. Builds on 15 of its 20 references that are in the map. First posted on arXiv 22 Aug 2026.

Builds on

15 of 20 references.

References · 15

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3 more
  1. 2026Apple Neural Engine: Architecture, Programming, and Performancecited by 2
  2. 2026EdgeDiT: Hardware-Aware Diffusion Transformers for Efficient On-Device Image Generationcited by 1
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