TL;DR
- vLLM Production Stack, originally from academic research, is now an official open-source reference implementation of a cluster-wide, full-stack vLLM serving system. Features include K8s-native router, autoscaling, CR-based LoRA management, distributed KV sharing, monitoring, etc.
- Why does it matter to the industry?
- A vibrant OPEN community with official vLLM NATIVE support.
- Designed for K8s-NATIVE deployment with performance optimizations tailored for LLM applications.
Why vLLM Production Stack?
AGI isn’t just about better models–it is also about better systems to serve the models to the wide public so that everyone will have access to the new capabilities! In order to fully harness the power of Generative AI, every organization that take this AI revolution seriously needs to have access to the best LLM-serving infrastructure with high performance, availability, and with cost as low as possible.
vLLM Production Stack is borne out of a long-time academic collaboration between the vLLM team (UC Berkeley) and the LMCache team (UChicago), an research-inspired KV-cache optimization system (CacheGen, CacheBlend). As more vLLM and LMCache users ask us for help with deploying our projects in their production settings, we see a pressing need for an official reference implementation of a cluster-wide vLLM serving system. Thus, we released vLLM Production Stack in early January 2025.
Open Community + Official vLLM Support
The project welcomes EVERYONE to contribute. It has a growing and vibrant contributor community with over 30 active contributors from various companies worldwide, including IBM, Redhat, Lambda, HuggingFace, TensorChord, and more. The project’s contributors are regularly invited to speak about it by Alluxio, Anyscale, and various industry tech events. We maintain ongoing collaborations and seek to establish new synergistic relationships with ecosystem technologies, such as Alluxio. Check out our community meeting notes here.
Moreover, we ensure that the latest vLLM Production Stack is always compatible with the latest vLLM releases, thanks to the LMCache KV Connector support in vLLM (PR 1,2) in the upstream main-branch vLLM.
In short, as our community grows, people never need to worry whether their contributions to the production stack might conflict with the vLLM releases.
K8s-Native Deployment + Optimized Performance
When deploying Kubernetes-native solutions, operators often have to choose either “K8s-native” or “optimized performance.” With vLLM Production Stack, you can have BOTH K8s-native support AND OPTIMIZED performance.
With one-click installer, everyone can create a multi-vLLM-instance cluster in a K8s environment in 2 minutes.
It supports features for Kubernetes operators (e.g. CR-based router, LoRA adapter management, K8s-native autoscaling, distributed KV cache sharing via direct point-to-point sharing or hierarchical structure), while incorporating latest research from academia (CacheGen, CacheBlend).
In short, vLLM production stack showcases the promise when academic research and industry expertise join forces!
Cost Efficiency and Compute Optimization
Deploying LLMs in production isn’t just about technical capabilities—it’s about making economic sense. vLLM Production Stack delivers significant cost advantages through several key optimizations:
-
Efficient Resource Utilization: Our distributed KV cache sharing (based on LMCache) and LLM-aware routing reduces memory redundancy and compute wastage across replicas, allowing you to serve more concurrent users with the same hardware.
-
Intelligent Autoscaling: The system scales resources up and down based on actual demand patterns, eliminating wasteful over-provisioning while maintaining performance.
-
Real-world Cost Savings: Early adopters report 30-40% cost reduction in real-world deployment compared to traditional serving solutions while maintaining or improving response times.
These efficiency gains translate directly to your bottom line. Whether you’re a startup watching every dollar or an enterprise managing large-scale deployments, vLLM Production Stack helps maximize the return on your AI investment.
Concluding Words
We also welcome more people to join us to build a future where every application can harness the power of LLM inference—reliably, at scale, and without breaking a sweat.
Contact us in the #production-stack channel or LMCache slack today to discuss the future steps!
Happy deploying!
Contacts:
- Github: https://github.com/vllm-project/production-stack
- Chat with the Developers: Interest Form
- vLLM Production-Stack channel
- LMCache slack