Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
Modern Intel AI PCs ship capable integrated GPUs and NPUs with 16+ GB of unified memory, and they spend considerable time idle. That is not enough memory to fit a large model such as a 70B-parameter LLM. We show that a handful of AIPCs, working together over an ordinary network, can serve models beyond the capability of any single one. We use pipeline parallelism: a model is split by layer into per-stage shards, each pre-compiled into an OpenVINO graph, so that every machine runs one shard and passes activations to the next. Three techniques make this fast enough to be useful. First, we recover the speed of the unsplit model: a naive per-stage export runs well below monolithic inference b...
| Authors | Tate Berenbaum, Muthaiah Venkatachalam |
|---|---|
| Primary category | cs.DC |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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