Alluxio & vLLM join forces for smarter AI inference

Alluxio has announced a strategic partnership with the vLLM Production Stack to enhance the infrastructure for large language model (LLM) inference.

The partnership seeks to address the unique demands of AI inference, characterised by the need for low-latency, high-throughput, and random access for handling large-scale read and write workloads. This joint venture is set to tackle these challenges amid increasing cost considerations for LLM-serving infrastructure.

Alluxio and vLLM Production Stack will collaborate to improve LLM inference performance through an integrated solution for KV Cache management. Alluxio's platform uniquely utilises both DRAM and NVME, offering better management tools and hybrid multi-cloud support. This enables an efficient sharing of KV Cache across various computing and storage layers, thereby improving scalability and efficiency for AI inference workloads.

Junchen Jiang, Head of LMCache Lab at the University of Chicago, stated, "Partnering with Alluxio allows us to push the boundaries of LLM inference efficiency. By combining our strengths, we are building a more scalable and optimised foundation for AI deployment, driving innovation across a wide range of applications."

Professor Ion Stoica, Director of Sky Computing Lab at the University of California, Berkeley, remarked, "The vLLM Production Stack showcases how solid research can drive real-world impact through open sourcing within the vLLM ecosystem. By offering an optimised reference system for scalable vLLM deployment, it plays a crucial role in bridging the gap between cutting-edge innovation and enterprise-grade LLM serving."

The joint solution from Alluxio and vLLM Production Stack speeds up the Time to First Token by saving the recomputation cost of previously seen queries. Through expanded cache capacity, the solution utilises CPU/GPU memory and NVMe to store partial results, thereby delivering quicker average response times.

The solution also enhances the storage of KV Cache across GPU/CPU memory and distributed storage layers, which is vital for handling large context windows and complex agentic workflows in LLMs.

The approach to KV Cache storage in an additional Alluxio service layer allows more efficient sharing between prefiller and decoder machines, reducing redundant computations. By leveraging mmap or zero-copy technology, this setup facilitates efficient KV Cache transfers, minimising memory copies and I/O overhead.

The collaboration also positions itself as a cost-effective solution by using NVMe to expand KVCache storage, offering lower unit costs compared to traditional DRAM-only configurations. The use of commodity hardware via Alluxio delivers similar performance to that of more expensive parallel file systems.

Bin Fan, Vice President of Technology at Alluxio, said, "This collaboration unlocks new possibilities for enhancing LLM inference performance, particularly by addressing the critical need for high-throughput low-latency data access. We are tackling some of AI's most demanding data and infrastructure challenges, enabling more efficient, scalable, and cost-effective inference across a wide range of applications."