CAIS delivers 1.38x end-to-end LLM training speedup over NVLS and 1.61x over T3 by making in-switch computing aware of computation memory requirements instead of treating communication as an isolated phase.
Zero: Memory optimizations toward training trillion parameter models,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Transferring a 2D MLLM to 3D CT inputs via parameter reuse, a Text-Guided Hierarchical MoE framework, and two-stage training yields better performance than prior 3D medical MLLMs on medical report generation and visual question answering.
ProTrain automates memory management for LLM training via cost models from profiling to deliver 1.43x-2.71x throughput gains over state-of-the-art systems without accuracy loss.
citing papers explorer
-
Towards Compute-Aware In-Switch Computing for LLMs Tensor-Parallelism on Multi-GPU Systems
CAIS delivers 1.38x end-to-end LLM training speedup over NVLS and 1.61x over T3 by making in-switch computing aware of computation memory requirements instead of treating communication as an isolated phase.
-
Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis
Transferring a 2D MLLM to 3D CT inputs via parameter reuse, a Text-Guided Hierarchical MoE framework, and two-stage training yields better performance than prior 3D medical MLLMs on medical report generation and visual question answering.
-
ProTrain: Efficient LLM Training via Memory-Aware Techniques
ProTrain automates memory management for LLM training via cost models from profiling to deliver 1.43x-2.71x throughput gains over state-of-the-art systems without accuracy loss.