Red Hat AI, a leading open-source enterprise technology provider, has launched llm-d, a Kubernetes-native distributed inference framework designed to overcome production-scale AI deployment challenges. Built on the popular open-source vLLM framework, llm-d enables faster, cost-efficient inference by transcending the limitations of single-server setups.
Developed collaboratively with tech giants like Google Cloud, IBM Research, NVIDIA, AMD, Cisco, and Intel, llm-d optimizes AI model serving in GPU-intensive data centers. Key architectural innovations include Prefill and Decode Disaggregation, which separates input context processing from token generation, allowing parallelization across servers. Additionally, KV Cache Offloading reduces GPU memory load by shifting cache storage to CPU or network memory, enhancing cost-efficiency.
In benchmark tests on NVIDIA H100 clusters, llm-d achieved a 3x faster time-to-first-token and 50–100% higher query throughput compared to baseline solutions, enabling rapid and reliable AI responses at scale. Google Cloud’s early testing also showed a 2x improvement in code completion tasks.
Designed for Kubernetes-powered environments, llm-d supports AI-aware network routing and multi-hardware compatibility, including NVIDIA, Google TPU, AMD, and Intel.
Red Hat emphasizes llm-d as the future of scalable GenAI inference, enabling enterprises to deploy production-grade AI efficiently without building custom monolithic systems.