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Kubernetes Native llm-d Could Be a ‘Turning Point in Enterprise AI’ for Inferencing
- Red Hat AI introduced llm-d, a Kubernetes-native distributed inference framework to address challenges in deploying AI models in production-ready environments.
- Developed in collaboration with tech giants like Google Cloud, IBM Research, NVIDIA, and others, llm-d optimizes AI model serving in demanding environments with multiple GPUs.
- llm-d's architecture includes techniques like Prefill and Decode Disaggregation and KV Cache Offloading to boost efficiency and reduce memory usage on GPUs.
- With Kubernetes-powered clusters and controllers, llm-d achieved significantly faster response times and higher throughput compared to baselines in NVIDIA H100 clusters.
- Google Cloud reported 2x improvements in time-to-first-token with llm-d for use cases like code completion, enhancing application responsiveness.
- llm-d features AI-aware network routing, supports various hardware like NVIDIA, Google TPU, AMD, and Intel, and aids in efficient scaling of AI inference.
- Industry experts believe llm-d by Red Hat could mark a turning point in Enterprise AI by enhancing production-grade serving patterns using Kubernetes and vLLM.
- Companies focus on scaling AI inference solutions, with efforts from hardware providers like Cerebras, Groq, and SambaNova aiming to accelerate AI inference in data centers.
- Recent research efforts have also been made in software frameworks and architectures to optimize AI inference, with advancements in reducing pre-fill compute and improving serving throughput.
- A study by Huawei Cloud and Soochow University reviewed efficient LLM inference serving methods at the instance level and cluster level, addressing various optimization techniques.
- vLLM introduced a 'Production Stack' for Kubernetes native deployment, focusing on distributed KV Cache sharing and intelligent autoscaling to reduce costs and improve response times.
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