What is the difference in token generation throughput (tokens/sec) between PowerInfer and standard vLLM infere

Abstract

Abstract: This article surveys Cognitive Edge Computing as a practical and methodical pathway for deploying reasoning-capable Large Language Models (LLMs) and autonomous AI agents on resource-constrained devices at the network edge. We present a unified, cognition-preserving framework spanning: (1) model optimization (quantization, sparsity, low-rank adaptation, distillation) aimed at retaining multi-step reasoning under tight memory/compute budgets; (2) system architecture (on-device inference, elastic offloading, cloud-edge collaboration) that trades off latency, energy, privacy, and capacity; and (3)

Research Question

What is the difference in token generation throughput (tokens/sec) between PowerInfer and standard vLLM inference for multimodal models like LLaVA on consumer-grade hardware with limited VRAM?

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