VQV Signal
Prefill-decode disaggregation and roofline-based operator placement are common but often insufficient for partitioning LLM inference across heterogeneous platforms. The paper introduces DOPS, a hardware-aware, closed-loop framework that accounts for workload shape, device contention, and weight lay...
Prefill-decode disaggregation and roofline-based operator placement are common but often insufficient for partitioning LLM inference across heterogeneous platforms. The paper introduces DOPS, a hardware-aware, closed-loop framework that accounts for workload shape, device contention, and weight lay...
AI-assisted summary based on the listed source.
Prefill-decode disaggregation (PD) and roofline-based operator placement are common strategies for partitioning Large Language Model (LLM) inference across heterogeneous systems, but they are often insufficient in practice. End-to-end latency also depends on workload shape, runtime device contention, and...
Improving LLM inference efficiency on heterogeneous hardware is critical for reducing latency and optimizing resource use. DOPS offers a more adaptive scheduling approach that addresses practical runtime factors beyond traditional methods.
Hardware and robotics watchers may want to track whether this becomes a product, benchmark, or deployment signal.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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