Alluxio Partners with vLLM Production Stack to Accelerate LLM Inference

Alluxio, Inc. announced a strategic collaboration with the vLLM Production Stack, an open-source implementation of a cluster-wide full-stack vLLM serving system developed by LMCache Lab, University of Chicago.

This partnership aims to advance the next-gen AI infrastructure for large language model (LLM) inference.

The rise of AI inference has reshaped data infrastructure demands, presenting distinct challenges compared to traditional workloads. Inference requires low-latency, high-throughput, and random access to handle large scale read and write workloads. With recent disruptions, costs have also become an important consideration for LLM-serving infrastructure.

To meet these unique requirements, Alluxio has collaborated with the vLLM Production Stack to accelerate LLM inference performance by providing an integrated solution for KV Cache management. The company is uniquely positioned to be a solution for KV Cache management because the firm enables larger capacity by utilizing both DRAM and NVMe, provides better management tools such as unified namespace and data management service, and offers hybrid multi-cloud support. This joint solution moves beyond traditional 2-tier memory management, enabling efficient KV Cache sharing across GPU, CPU, and a distributed storage layer. By optimizing data placement and access across different storage tiers, it delivers low-latency, greater scalability, and improved efficiency for large-scale AI inference workloads.

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

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

Alluxio and vLLM Production Stack joint solution highlights:

• Accelerated Time to First Token
  KVCache is a key technique to accelerate the user perceived response time of an LLM query, (Time-To-First-Token). By storing complete or partial results of previously seen queries, it saves the recomputation cost when part of the prompt has been processed before, a common occurrence in LLM inference. Alluxio expands the capacity of LLM serving systems to cache more of these partial results by using CPU/GPU memory and NVMe, which leads to faster average response time.
• Expanded KV Cache Capacity for Complex Agentic Workloads
  Large context windows are key to complex agentic workflows. The joint solution can flexibly store KVCache across GPU/CPU memory and a distributed caching layer (NVMe-backed Alluxio). This is critical for long context use cases of LLMs.
• Distributed KV Cache Sharing to Reduce Redundant Computation:
  Storing KV Cache in an additional Alluxio service layer instead of locally on the GPU machines allows prefiller and decoder machines to share the same KV Cache more efficiently, By leveraging mmap or zero-copy technology, the joint solution enhances inference throughput by enabling efficient KV Cache transfers between GPU machines and Alluxio, minimizing memory copies and reducing I/O overhead. It is also more cost effective as storage options on GPU instances are limited and expensive.
• Cost-effective High Performance:
  The joint solution provides expanded KVCache storage at a lower cost of ownership. Compared to a DRAM-only solution, Alluxio utilizes NVMe which offers lower unit cost per byte. Instead of other parallel file systems, Alluxio can leverage commodity hardware to provide similar performance.

“This collaboration unlocks new possibilities for enhancing LLM inference performance, particularly by addressing the critical need for high-throughput low-latency data access,” said Bin Fan, VP, technology, Alluxio. “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.“

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