DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving (2411.02820v3)

Abstract: LLMs are increasingly employed in complex workflows, where different LLMs and fine-tuned variants collaboratively address complex tasks. However, these systems face significant inefficiencies due to redundant context processing of the shared context. We propose DroidSpeak, a framework that optimizes context sharing between fine-tuned LLMs derived from the same foundational model. DroidSpeak identifies critical layers in the KV cache and selectively recomputes them, enabling effective reuse of intermediate data while maintaining high accuracy. Our approach balances computational efficiency and task fidelity, significantly reducing inference latency and throughput bottlenecks. Experiments on diverse datasets and model pairs demonstrate that DroidSpeak achieves up to 3x higher throughputs and 2.6x faster prefill times with negligible accuracy loss compared to full recomputation.

• The paper proposes a caching strategy that reuses key intermediate computations, reducing LLM prefill latency by up to 2.78×.
• It employs layer-wise profiling to determine optimal reuse, preserving accuracy across fine-tuned models.
• The study's findings enhance multi-agent system efficiency and pave the way for responsive real-time AI applications.

Enhancing Multi-Agent System Efficiency with DroidSpeak

The paper explores the persistent challenge of high prefill latency in communication among LLMs within multi-agent systems. Traditional inter-agent communication typically employs natural language protocols, resulting in significant prefill-phase overhead, especially as contexts lengthen. The authors propose a novel communication framework named DroidSpeak, designed to address this inefficiency.

DroidSpeak introduces a strategic approach to minimize the repeated contextual processing characteristic of traditional communications among agents utilizing fine-tuned versions of the same foundational LLM. The key innovation lies in the reuse of intermediate computations such as input embeddings (E-cache) and key-value caches (KV-cache), streamlining the communication phase by reducing the necessity to entirely regenerate contexts.

Numerical Results and Implications

The paper presents compelling experimental results, demonstrating that DroidSpeak achieves a speedup in prefill latency by up to 2.78×, with negligible loss in accuracy compared to traditional methods. These findings are crucial as they highlight the potential to significantly enhance the scalability and responsiveness of multi-agent systems without compromising task performance quality.

The implications of this research extend beyond the immediate improvements in system efficiency. By optimizing the integration of contexts, DroidSpeak paves the way for developing more sophisticated and responsive AI-driven applications, where agents need to perform in real-time.

Methodological Insights

The paper meticulously analyzes the similarities and differences among fine-tuned LLMs sharing a common foundational model. It discovers variations in the impact on accuracy across different layers, which informs the selective reutilization approach in DroidSpeak. This insight underscores the utility of intermediate data layers, particularly in systems that utilize high-bandwidth interconnects typical in cloud environments, allowing computational resources to be prioritized elsewhere.

Additionally, DroidSpeak introduces an intelligent profiling phase to determine which layers to reuse effectively. This layer-wise optimization is fundamental to achieving the reported latency reductions without significant performance degradation.

Theoretical and Practical Development

Theoretically, DroidSpeak's framework advances the understanding of communication efficiency in LLM-based systems, proposing a scalable model adaptable to various applications. Practically, it offers a substantive improvement in latency, which is critical in applications where real-time processing is non-negotiable, such as customer service automation and collaborative workflows in content creation or code development.

Future Developments

While DroidSpeak significantly reduces prefill latency in systems with fine-tuned LLMs, future research could extend these efficiency gains to more diverse model types, including those differing in architecture and configuration. Additionally, integrating sophisticated compression techniques for KV and E caches might further optimize transmission times, offering another avenue for enhancing multi-agent communication protocols.

In conclusion, this paper presents significant advancements in reducing the inefficiencies inherent in inter-agent communications among LLMs, thus contributing substantially to the field of artificial intelligence and multi-agent systems. By proposing and validating DroidSpeak, the authors contribute a practical and theoretical framework poised to influence future developments in creating faster, more efficient AI systems.