Disaggregated LLM inference architectures separate prefill, decode, and router stages into independent Kubernetes services, enabling fine-grained resource allocation, independent scaling, and improved GPU utilization tailored to the distinct compute and memory bandwidth requirements of each stage.
Our findings illustrate that these complementary components (Kueue, DAS, and GAIE) complement core Kubernetes to form a cohesive, high-performance platform, proving Kubernetes’ capability to serve as a unified foundation for demanding GenAI inference workloads.
These platforms must provide low latency and high bandwidth to ensure efficient data handling and processing. As the size and complexity of datasets grow, the networking infrastructure must scale accordingly to maintain performance and reliability.
This comprehensive guide delves into the intricacies of latency reduction, exploring foundational concepts, common causes of latency, and best practices tailored for Kubernetes-based custom workflows.
Why latency matters in Kubernetes Running latency-sensitive workloads on Kubernetes is possible but only with deliberate configuration. This is critical in fields like high-frequency trading, real-time market data, telecoms, online gaming, industrial control systems and real-time AI inference, where microseconds matter. By default, Kubernetes optimises for fairness and multi-tenancy, not deterministic performance. For systems where jitter and noisy neighbours impact outcomes, the defaults ...
Guidance for Low Latency, High Throughput Inference using Efficient Compute on Amazon EKS The guidance-for-machine-learning-inference-on-aws repository contains an end-to-end automation framework example for running model inference locally on Docker or at scale on Amazon EKS Kubernetes cluster.
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