Revolutionizing AI Efficiency: UC Berkeley’s SqueezeLLM Debuts Dense-and-Sparse Quantization, Marrying Quality and Speed in Large Language Model Serving

Recent developments in Large Language Models (LLMs) have demonstrated their impressive problem-solving ability across several fields. LLMs can include hundreds of billions of parameters and are trained on enormous text corpora.  Studies show that in LLM inference, memory bandwidth, not CPU, is the key performance limitation for generative tasks. This indicates that the rate at […]

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