GOODSPEED

GOODSPEED explores speculative decoding for efficient large language model inference in distributed edge environments.

The project studies how to balance inference speed, output quality, and proportional fairness across heterogeneous edge resources and draft servers.

Research Focus

• Speculative decoding optimization for faster token generation while preserving output quality.
• Fair resource allocation across heterogeneous edge clients and servers.
• Efficient LLM inference in resource-constrained distributed systems.
• Coordinated inference architectures across multiple edge nodes.

Research Areas

GOODSPEED builds on recent advances in accelerated inference, distributed machine learning systems, edge computing optimization, and fair resource allocation.

Related publication: (Tran et al., 2025)

Status

This project is under active development. Research findings and implementation details will be shared as they become available.