Automated Tensor Scheduling for Hybrid CPU-GPU LLM Inference on Consumer Devices

2026-07-11 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

ATSInfer is a novel hybrid CPU-GPU inference system designed to enable large language models (LLMs) on consumer devices by overcoming GPU memory limitations. It addresses the shortcomings of existing offloading systems, which typically use coarse layer- or expert-level scheduling, by implementing offloading at a finer tensor granularity. ATSInfer integrates static tensor placement with load-aware dynamic transfer and employs asynchronous CPU-GPU coordination to efficiently manage hardware storage, data movement, and computation across heterogeneous backends. Evaluated on representative consumer platforms with both dense and Mixture-of-Experts (MoE) models, ATSInfer significantly improves prefill throughput by up to 1.94x and decode throughput by up to 3.29x. This system also enhances GPU utilization and makes more effective use of PCIe bandwidth, leading to a substantially improved user experience for local LLM deployment.

Key takeaway

For AI engineers deploying LLMs on consumer hardware, ATSInfer offers a significant performance uplift. You should consider systems employing tensor-level offloading and dynamic CPU-GPU coordination to overcome VRAM limitations. This approach substantially improves prefill and decode throughput, enhancing the local LLM user experience and making larger models viable on personal devices.

Key insights

ATSInfer optimizes LLM inference on consumer devices via fine-grained, dynamic CPU-GPU tensor scheduling.

Principles

• Tensor-level granularity improves offloading efficiency.
• Combine static placement with dynamic load adaptation.
• Asynchronous coordination optimizes heterogeneous hardware.

Method

ATSInfer uses static tensor placement with load-aware dynamic transfer and asynchronous CPU-GPU coordination to schedule storage, data movement, and computation across heterogeneous backends.

In practice

• Deploy LLMs on consumer devices with limited GPU VRAM.
• Improve throughput for both dense and MoE models.
• Enhance GPU utilization and PCIe bandwidth use.

Topics

• LLM Inference
• CPU-GPU Hybrid
• Tensor Scheduling
• Consumer Devices
• Offloading
• Distributed Computing

Best for: NLP Engineer, AI Scientist, Research Scientist, Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.