Sarah S.

Responsibilities:

• • Contributed to an R&D team developing data-center-scale infrastructure for high-performance LLM inference.

• • Deep Exploration of vLLM & LLM-D Architecture Led a comprehensive exploration of vLLM’s internal mechanisms and execution model, alongside an architectural breakdown of llm-d. This included mapping request lifecycles, understanding component interactions, and characterizing the full behavior of the inference pipeline from input to output.
• • KV-Cache Optimization in a Hyper-Converged Environment Engineered a hyper-converged setup for KV-Cache offloading, leveraging LLM-D and LMCache to streamline cache handling and KVrocks for persistent key-value storage. Implemented memory- and system-level optimizations that improved cache locality and boosted inference stability and throughput
• • GPU Performance Profiling & Multi-Pipeline Benchmarking Executed rigorous profiling sessions on advanced GPU clusters, evaluating three different inference pipelines. Identified performance constraints and delivered up to 2× gains in TPS and RPS per user when comparing LMCache-backed (DRAM-cached) runs against baseline vLLM execution paths.
• • Kubernetes-Based Deployments (Single-Node & Multi-Node) Implemented both single-node and multi-node deployments of LLM-D and LMCache on Kubernetes clusters, enabling reproducible performance testing, streamlined rollout strategies, and consistent behavior across environments
• • Hyper-Converged Routing Simplification & System Streamlining Redesigned routing behavior by removing storage-aware routing dependencies, enabling a truly hyper-converged inference architecture. This optimization significantly reduced network overhead, simplified data paths, and improved the system’s operational efficiency.