arXiv:2605.01708v3 Announce Type: replace-cross Abstract: Contemporary systems serving large language models (LLMs) have adopted prefill-decode disaggregation to load-balance between the compute-bound prefill phase and the memory-bound decode phase. Under this design, prefill workers generate a KV cache that must be transferred to decode workers before generation can begin. With these workers residing on different physical systems, this transfer becomes a significant bottleneck to serving LLMs at scale, especially for long-input and agentic workloads. Existing lossless codecs are unsuitable he
The increasing complexity and scale of Large Language Models (LLMs), particularly for long-input and agentic workloads, are pushing the limits of current serving architectures, necessitating innovation in data transfer efficiency.
Efficient KV cache compression directly addresses a critical bottleneck in scaling LLM inference, enabling more cost-effective and performant deployment of advanced AI applications.
The ability to transfer KV caches between disaggregated LLM workers significantly faster reduces latency and increases throughput, allowing for more complex and larger-scale AI deployments.
- · Cloud providers
- · LLM developers
- · AI infrastructure companies
- · Companies with inefficient LLM serving architectures
Reduced operational costs and improved performance for large-scale LLM inference due to faster data transfer.
Acceleration in the development and deployment of agentic AI systems and applications requiring long context windows.
Potentially enables new classes of AI applications that were previously infeasible due to computational and memory bandwidth constraints.
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Read at arXiv cs.LG