Principle:Intel Ipex llm Pipeline Parallel Model Loading
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, NLP, Model_Loading |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Technique for loading a quantized language model with its layers automatically distributed across multiple Intel GPUs.
Description
Pipeline Parallel Model Loading uses the pipeline_parallel_stages parameter to automatically partition a model's transformer layers across multiple Intel XPU devices. Combined with low-bit quantization (SYM_INT4 by default), this enables inference on models that exceed single-GPU memory. Each GPU holds a contiguous subset of layers and processes activations sequentially.
Usage
Use this after init_pipeline_parallel() when deploying models too large for a single GPU. The gpu_num parameter determines how many pipeline stages (GPUs) the model is split across.
Theoretical Basis
# Abstract layer distribution (NOT real implementation)
# For a model with 40 layers on 2 GPUs:
# GPU 0: embedding + layers 0-19
# GPU 1: layers 20-39 + lm_head
# Forward: GPU0 computes first half, sends activations to GPU1
# GPU1 computes second half and produces logits
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