GhostServe applies erasure coding to KV cache in host memory for fast recovery from failures in LLM serving, cutting checkpointing latency up to 2.7x and recovery latency 2.1x versus prior methods.
Understanding stragglers in large model training using what-if analysis
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Production-scale empirical study of a 63-node 504-GPU cluster reports multi-signal failure detection needs, low checkpoint bandwidth utilization, heavy-tailed node exclusions, and 2.7x higher success for auto-retry chains.
ResiHP introduces a workload-aware failure detector and dynamic scheduler for hybrid-parallel LLM training that achieves 1.04-4.39x higher throughput than prior resilient systems under failures on a 256-GPU cluster.
citing papers explorer
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GhostServe: A Lightweight Checkpointing System in the Shadow for Fault-Tolerant LLM Serving
GhostServe applies erasure coding to KV cache in host memory for fast recovery from failures in LLM serving, cutting checkpointing latency up to 2.7x and recovery latency 2.1x versus prior methods.
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From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs
Production-scale empirical study of a 63-node 504-GPU cluster reports multi-signal failure detection needs, low checkpoint bandwidth utilization, heavy-tailed node exclusions, and 2.7x higher success for auto-retry chains.
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ResiHP: Taming LLM Training Failures with Dynamic Hybrid Parallelism
ResiHP introduces a workload-aware failure detector and dynamic scheduler for hybrid-parallel LLM training that achieves 1.04-4.39x higher throughput than prior resilient systems under failures on a 256-GPU cluster.