Large Language Models (LLMs) have rapidly transitioned from a nascent concept to a pervasive force in machine learning, enabling sophisticated applications from conversational AI to content generation. However, the deployment of these powerful
models at scale presents significant challenges, notably high latency and inefficient resource utilization. Traditional inference pipelines often struggle with the immense computational and memory demands of LLMs, leading to slow response times and
prohibitive operational costs. This is where vLLM, standing for Virtual Large Language Model, emerges as a transformative solution. Developed initially at the Sky Computing Lab at UC Berkeley, vLLM is an open-source library designed to optimize
and accelerate LLM inference and serving, ensuring faster and more cost-effective deployment.

vLLM primarily tackles critical scaling challenges through innovative memory management and parallelization techniques. Its flagship feature, PagedAttention, revolutionizes the management of the attention key-value (KV) cache. Unlike
traditional methods that require contiguous memory blocks, PagedAttention breaks the KV cache into non-contiguous blocks, akin to virtual memory in operating systems. This dramatically reduces memory fragmentation, allowing for more efficient
memory reuse and significantly improving throughput—by some estimates, up to 24 times higher compared to other popular open-source libraries like Hugging Face Transformers. Complementing PagedAttention is continuous batching, which
dynamically processes incoming requests, keeping the GPU highly utilized and minimizing idle compute time. These innovations collectively address memory constraints, token-by-token generation overhead, and inefficient batching that plague traditional LLM inference systems. Additional optimizations include asynchronous prefetching, optimized CUDA kernels, quantization support, and speculative decoding.

Yet as LLM use cases grow, there is a need for not just fast, but distributed, production-ready inference in cloud-native environments. Enter llm-d, a Kubernetes-native distributed LLM inference framework, launched in collaboration
with Red Hat, Google, CoreWeave, IBM, and others. llm-d is designed for:

• Disaggregated serving: Splitting prefill (prompt processing) and decode (token
  generation) phases across specialized workloads to optimize GPU/accelerator
  use and lowest latency.
• KV-cache aware routing: Enabling request scheduling that utilizes existing
  cache hits, reducing redundant computation and response time.
• Scalable, modular clusters: Seamlessly orchestrated within Kubernetes, llm-d
  empowers enterprises to deploy LLM inference clouds that meet strict SLAs
  while adapting to any infrastructure, model, or accelerator.developers.redhat+2

Join this session for a technical deep-dive: what enables the efficiency of vLLM, how llm-d operationalizes LLM inference at scale, and how this ecosystem is driving the next frontier of accessible, production-grade AI.