Efficient Serving Infrastructure (KServe and llm-d)

Why?

• High-throughput, low-latency inference at scale depends on infrastructure optimized for model serving, autoscaling, and efficient resource usage.
• Modern AI workloads need unified serving platforms that support both traditional ML models and LLM-specific deployment patterns.

What?

• Evaluate KServe for Kubernetes-native model serving with advanced autoscaling, multi-framework support, and built-in GPU/CPU optimization.
• Assess llm-d for efficient LLM deployment, lightweight model serving, and inference acceleration across clouds and edge environments.
• Standardize service abstractions, observability, and rollout patterns for production inference, including canary deployments, traffic splitting, and platform-integrated monitoring.

Source

• KServe: https://kserve.github.io/
• llm-d: https://github.com/intel/llm-d