LLM Inference Scaling Beyond Limits
New research explores scaling LLM inference beyond Amdahl's limits by eliminating non-scalable overheads. Deployers of online LLM services aim to maximize cluster-wide performance with a fixed number of GPUs, where tensor parallelism is necessary.
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Developing
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Sources · 7 independent
Modernity/arxiv
“Scaling LLM Inference Beyond Amdahl`s Limits via Eliminating Non-Scalable Overheads. Authors: Alan Zhao, Cyril Y. He, Wei Xu Abstract: Deployers of online LLM services usually seek to maximize cluster-wide performance given a fixed number of GPUs. Tensor parallelism (TP) is necessary...”
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