Do Large Language Models Need a Content Delivery Network? (2409.13761v2)

Abstract: As the use of LLMs expands rapidly, so does the range of knowledge needed to supplement various LLM queries. Thus, enabling flexible and efficient injection of new knowledge in LLM inference is critical. Three high-level options exist: (i) embedding the knowledge in LLM's weights (i.e., fine-tuning), (ii) including the knowledge as a part of LLM's text input (i.e., in-context learning), or (iii) injecting the KV caches of the new knowledge to LLM during prefill. This paper argues that, although fine-tuning and in-context learning are popular, using KV caches as the medium of knowledge could simultaneously enable more modular management of knowledge injection and more efficient LLM serving with low cost and fast response. To realize these benefits, we envision a Knowledge Delivery Network (KDN), a new system component in LLM services that dynamically optimizes the storage, transfer, and composition of KV cache across LLM engines and other compute and storage resources. We believe that, just like content delivery networks (CDNs), such as Akamai, enabled the success of the Internet ecosystem through their efficient data delivery, KDNs will be critical to the success of LLM applications through their efficient knowledge delivery. We have open-sourced a KDN prototype at https://github.com/LMCache/LMCache.

• The paper proposes the Knowledge Delivery Network, a novel architecture that decouples knowledge management via KV caches from LLM inference.
• The paper demonstrates how the tri-layered system—storage, delivery, and blending—overcomes limitations of fine-tuning and in-context learning by enhancing efficiency and modularity.
• The paper discusses key trade-offs in latency and resource usage, highlighting cache compression and offline preprocessing as practical techniques for scalable LLM deployment.

Do LLMs Need a Content Delivery Network?

The paper "Do LLMs Need a Content Delivery Network?" presents a forward-looking examination of integrating content delivery strategies into the infrastructure supporting LLMs. As the use of LLMs proliferates, there is a burgeoning requirement to incorporate dynamic external knowledge into the model's inference process. The authors propose a novel architecture termed the Knowledge Delivery Network (KDN), which mirrors the successful Content Delivery Networks (CDNs) that facilitate efficient data delivery across the internet. This paper explores the viability of using KDNs to optimize the deployment and serving efficiency of LLMs by utilizing Key-Value (KV) caches as the medium for knowledge integration.

Key Aspects of the Proposed Architecture

At its core, the KDN aims to manage and optimize the storage, delivery, and use of KV caches to enhance LLM inference performance. This architecture proposes a tri-layered system:

1. Storage Module: This involves storing KV caches associated with varying text content. The module allows for offline editing, aiming to preemptively enhance the quality of responses generated during inference.
2. Delivery Module: Responsible for the efficient transmission of KV caches from storage systems to execution environments. Emerging techniques such as cache compression substantiate the viability of scaling KV cache use without prohibitive increases in operational overhead.
3. Blending Module: This system component dynamically compiles multiple KV caches, addressing a significant challenge in conventional KV cache reusability, which traditionally depends on prefix constraints.

System Trade-offs: Modularity and Efficiency

The KDN model is posited as addressing key trade-offs in the knowledge-injection domain. Current paradigms—such as fine-tuning and in-context learning—exhibit limitations in either flexibility or computational efficiency:

• Fine-tuning embeds additional knowledge into the model's parameters, limiting the on-the-fly specification of context but providing low inference latency once deployed.
• In-context learning provides modularity but incurs significant inference delays due to the necessary computational burden of processing extended input lengths.

The introduction of KV cache learning, particularly through a dedicated KDN, offers potential improvements in both modularity and efficiency. By separating knowledge management (in KV caches) from the model's core inference engine, KDN facilitates adaptable, resource-efficient operations.

Implications and Future Directions

The implications for KDN implementation are substantial. By decoupling knowledge management from LLM engines, the architecture promises performance gains in handling dynamic content and supporting expansive LLM applications with reduced latency and resource usage.

The proposed KDN framework paves the way for applying emergent cache optimization techniques within LLM infrastructures. These include KV cache compression, blending, and offline preprocessing strategies that could enhance both system performance and response quality.

The anticipatory design calls for integration with existing LLM serving mechanisms, welcoming ongoing contributions from the machine learning system community to refine the implementation procedures, interaction models, and performance benchmarks.

In conclusion, the integration of Knowledge Delivery Networks represents a promising pathway for reimagining how LLMs handle and utilize dynamic, external knowledge effectively. By embedding efficiencies similar to those achieved by CDNs, future LLM infrastructure can better accommodate the growing demand for adaptivity and speed in processing diverse input sources. This approach not only optimizes the computation-resource trade-off but also enhances the utility and application scope of LLMs across different domains.