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Found 2 papers, 1 papers with code
Seesaw: High-throughput LLM Inference via Model Re-sharding
To improve the efficiency of distributed large language model (LLM) inference, various parallelization strategies, such as tensor and pipeline parallelism, have been proposed.
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
We implement Tempo and evaluate the throughput, memory usage, and accuracy/loss on the BERT Large pre-training task.
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