Quantize-Sample-and-Verify: LLM Acceleration via Adaptive Edge-Cloud Speculative Decoding

TL;DR

This paper introduces a quantization strategy for edge-cloud speculative decoding that preserves output distribution, enabling adaptive optimization of token throughput and significantly improving decoding efficiency in edge-cloud LLM systems.

Contribution

The paper proposes a novel quantize-sample method that maintains distribution fidelity and an adaptive mechanism for optimizing token throughput based on channel and semantic conditions.

Findings

Significant improvement in decoding efficiency demonstrated in simulations.

Quantize-sample preserves the output distribution of large language models.

Adaptive mechanism effectively balances semantic uncertainty and communication constraints.

Abstract

In edge-cloud speculative decoding (SD), edge devices equipped with small language models (SLMs) generate draft tokens that are verified by large language models (LLMs) in the cloud. A key bottleneck in such systems is the limited communication bandwidth between edge and cloud, which necessitates quantization of the information transmitted about generated tokens. In this work, we introduce a novel quantize-sample (Q-S) strategy that provably preserves the output distribution of the cloud-based model, ensuring that the verified tokens match the distribution of those that would have been generated directly by the LLM. We develop a throughput model for edge-cloud SD that explicitly accounts for communication latency. Leveraging this model, we propose an adaptive mechanism that optimizes token throughput by dynamically adjusting the draft length and quantization precision in response to both semantic uncertainty and channel conditions. Simulations demonstrate that the proposed Q-S approach significantly improves decoding efficiency in realistic edge-cloud deployment scenarios.