What is the impact of PowerInfer's neuron activation sparsity on throughput and latency when running LLaMA-33B versus LLaMA-70B on consumer-grade GPUs

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

Research Question

What is the impact of PowerInfer's neuron activation sparsity on throughput and latency when running LLaMA-33B versus LLaMA-70B on consumer-grade GPUs?

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