Deploying Disaggregated LLM Inference Workloads on Kubernetes

As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages...

As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages have fundamentally different compute profiles, yet traditional deployments force them onto the same hardware, leaving GPUs underutilized and scaling inflexible. Disaggregated serving addresses this by splitting the inference pipeline…

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