5 posts tagged with "llm-d release news!"

In our 0.2 release, we introduced the first well-lit paths, tested blueprints for scaling inference on Kubernetes. With our 0.3 release, we double down on the mission: to provide a fast path to deploying high performance, hardware-agnostic, easy to operationalize, at scale inference.

This release delivers:

• Expanded hardware support, now including Google TPU and Intel support
• TCP and RDMA over RoCE validated for disaggregation
• A predicted latency based balancing preview that improves P90 latency by up to 3x in long-prefill workloads
• Wide expert parallel (EP) scaling to 2.2k tokens per second per H200 GPU
• The GA release of the Inference Gateway (IGW v1.0).

Taken together, these results redefine the operating envelope for inference. llm-d enables clusters to run hotter before scaling out, extracting more value from each GPU, and still meet strict latency objectives. The result is a control plane built not just for speed, but for predictable, cost-efficient scale.

The llm-d project provides a series of “well-lit paths” - tested, benchmarked solutions for deploying large language models in production. Our first path, Intelligent Inference Scheduling, established a baseline for AI-aware routing by balancing both cluster load and prefix-cache affinities. The default configuration for that path uses an approximate method for the latter, predicting cache locality based on request traffic.

This blog illuminates a more advanced and powerful path: precise prefix-cache aware scheduling.

We take a deep dive into the next generation of this feature, which moves beyond prediction and gives the scheduler direct introspection into distributed vLLM caches. This precision is key to maximizing cache hit rates and achieving a new level of performance and maximizing cost-efficiency in your distributed deployments.

The llm-d project lays out clear, “well-lit” paths for anyone to adopt the leading inference optimizations within their existing deployment framework - Kubernetes. These are tested approaches designed to make complex deployments easier and more efficient. In this post, we explore the first of these paths: intelligent inference scheduling. Unlike basic round-robin load balancing, this method takes the unique demands of LLMs into account, leading to better performance across the board: higher throughput, lower latency, and efficient use of resources.

Why Intelligent Inference Is Needed for LLM Inference​

Deploying large language models (LLMs) on Kubernetes has become the norm, but LLM inference workloads behave very differently from standard microservices. Traditional patterns like uniform replicas paired with round-robin load balancing assume each request uses the same amount of resources and finishes in roughly the same time. In contrast, LLM requests can vary wildly in token count and compute needs, making simple load-spread strategies prone to bottlenecks and imbalanced traffic.

Our 0.2 release delivers progress against our three well-lit paths to accelerate deploying large scale inference on Kubernetes - better load balancing, lower latency with disaggregation, and native vLLM support for very large Mixture of Expert models like DeepSeek-R1.

We’ve also enhanced our deployment and benchmarking tooling, incorporating lessons from real-world infrastructure deployments and addressing key antipatterns. This release gives llm-d users, contributors, researchers, and operators, clearer guides for efficient use in tested, reproducible scenarios.

Announcing the llm-d community​

llm-d is a Kubernetes-native high-performance distributed LLM inference framework
- a well-lit path for anyone to serve at scale, with the fastest time-to-value and competitive performance per dollar for most models across most hardware accelerators.

With llm-d, users can operationalize gen AI deployments with a modular, high-performance, end-to-end serving solution that leverages the latest distributed inference optimizations like KV-cache aware routing and disaggregated serving, co-designed and integrated with the Kubernetes operational tooling in Inference Gateway (IGW).