arXiv: cs.AI preprintsInternational6 November 2026
AutoCompress: Automated Hardware-Guided Model Compression
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arXiv:2611.08290v1 Announce Type: cross Abstract: LLM pruning must preserve quality while meeting hardware- and workload-specific constraints on latency, throughput, and memory. Existing pruning workflows often target parameter counts, requiring practitioners to manually select model sizes that meet latency, throughput, and GPU memory limits. However, parameter count alone does not predict these metrics, which depend on the model architecture, workload, hardware, and serving engine. We introduce AutoCompress, a hardware-guided compression framework that directly incorporates such constraints i
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