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

https://arxiv.org/abs/2607.10183v1

Core Idea

Problem: Running large language models on consumer devices is challenging because model weights exceed GPU memory, and existing offloading systems use coarse layer-level scheduling that ignores tensor heterogeneity and adapts poorly to changing hardware loads.

For this daily profile, it is worth opening because it links Inference, Language, and Model to a concrete method, not just a broad trend.

What Is New

The novelty signal is concentrated around Inference, Language, Model, and LLM. For this profile, the important question is whether the paper changes how architecture ideas are generated, evaluated, or connected to software and hardware constraints.

Methodology

Read this as a loop: define the target system, apply the proposed mechanism, measure against a baseline, then use the measured signal to justify the next design choice. Mechanism: Running large language models on consumer devices such as laptops and desktops is challenging because model weights often exceed GPU memory capacity, making offloading inference necessary to extend effective model capacity with CPU memory. Evidence: We implement ATSInfer and evaluate it on representative consumer platforms using both dense and MoE models.

score(design) = quality_metric(design) - cost_to_evaluate(design) + feedback_gain(design)

Figure To Read First

Read this visual first: focus on the first architecture, workflow, or pipeline figure before the experiments. It should show what is optimized, what feedback signal is used, and where the system boundary sits.

Minimal Mental Model

research artifact
  question      -> what design, runtime, or system boundary changes?
  mechanism     -> model, agent, compiler, simulator, or hardware feedback
  evaluation    -> baseline comparison plus cost / latency / accuracy signal
  reusable idea -> what should carry into the next architecture experiment?

Why It Matters

Paper recommendations matter when they sharpen the research map: what problem is now easier to study, what methodology becomes reusable, and which architecture assumptions should be questioned next.