What is the inference latency and throughput trade-off for LLaMA models of varying sizes (7B to 70B) when eval

Abstract

Abstract: This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key principle underlying the design of PowerInfer is exploiting the high locality inherent in LLM inference, characterized by a power-law distribution in neuron activation. This distribution indicates that a small subset of neurons, termed hot neurons, are consistently activated across inputs, while the majority, cold neurons, vary based on specific inputs. PowerInfer exploits such an insight to design a GPU-CPU hybrid inference e

Research Question

What is the inference latency and throughput trade-off for LLaMA models of varying sizes (7B to 70B) when evaluated on the HumanEval and GSM8K benchmarks, and how does this efficiency compare to state-of-the-art sparse or quantized models like GPTQ or SparseGPT?

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