UCCL-Zip adds lossless compression to GPU communication to reduce LLM bottlenecks while preserving exact numerical correctness.
Neuzip: Memory-efficient training and inference with dynamic compression of neu- ral networks.arXiv preprint arXiv:2410.20650,
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Thermodynamic lower bounds are approximated for exact and SGD linear regression, producing energy-aware scaling laws for optimal training dataset size given a target generalization error.
ZipCCL delivers up to 1.35x faster communication and 1.18x end-to-end speedup in LLM training through lossless compression of near-Gaussian collectives on 64-GPU clusters.
ZipMoE delivers up to 72.77% lower inference latency and 6.76x higher throughput for on-device MoE models via lossless compression and cache-affinity scheduling with a claimed provable guarantee.
citing papers explorer
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UCCL-Zip: Lossless Compression Supercharged GPU Communication
UCCL-Zip adds lossless compression to GPU communication to reduce LLM bottlenecks while preserving exact numerical correctness.
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The Thermodynamic Costs of Simple Linear Regression
Thermodynamic lower bounds are approximated for exact and SGD linear regression, producing energy-aware scaling laws for optimal training dataset size given a target generalization error.
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ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
ZipCCL delivers up to 1.35x faster communication and 1.18x end-to-end speedup in LLM training through lossless compression of near-Gaussian collectives on 64-GPU clusters.
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ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling
ZipMoE delivers up to 72.77% lower inference latency and 6.76x higher throughput for on-device MoE models via lossless compression and cache-affinity scheduling with a claimed provable guarantee.