Max Luong

Python, PyTorch, CUDA, MLOps (K8s), System Design, Transformer Architecture, Distributed Training, Inference Optimization (Quantization/KV Cache).

LLM System Tuning & Low-Level Inference. Studying GPU Memory Hierarchy (HBM/SRAM), OS Paging (PagedAttention logic), and Transformer internals (GQA/RoPE) for scalable serving.

Benchmarking inference servers. Focus on Compute-Bound vs. Memory-Bound analysis and implementing high-throughput scheduling (Continuous Batching) for deep learning models.

High-Performance LLM Serving, MLOps, and GPU Performance Tuning. Say Hey if you're optimizing latency, designing PagedAttention systems, or debugging Memory-Bound bottlenecks.