Splitting Model Layers Across the Internet

Running large language models across distributed consumer and rented GPUs turns network round-trips into the primary performance bottleneck. While frameworks like Shard, ShardFlow, and Mesh-LLM prove that scattered hardware can execute massive models without sacrificing deterministic accuracy, naive layer splitting across the public internet slows generation to a crawl. Overcoming those delays demands asynchronous pipelining, speculative decoding, and optimized routing paths, making the scheduler far more critical to real-world throughput than raw compute speed. Key sources: • Cunningham, Privacy-Aware Split Inference with Speculative Decoding for LLMs over WANs, 2026-02-18 — https://arxiv.org/abs/2602.16760 • working implementation of the above — https://github.com/coder903/split-inference • Shard, Apache-2.0, README + FLEET_STATE.md, accessed 2026-10-29 — https://github.com/leyten/shard Episode 6349 — myweirdprompts.com/n/6349 (permanent short link: myweirdprompts.com/208305)