Maher Hanafi is an engineering leader who went from zero AI experience to self-hosting LLMs at enterprise scale — managing GPU costs, optimizing inference with TensorRT LLM, and building an AI platform for HR tech. In this conversation, he breaks down exactly how his team cut latency by 70%, reduced GPU spend through counterintuitive scaling strategies, and navigated the messy reality of taking AI from proof-of-concept to production. How We Cut LLM Latency 70% With TensorRT in Production // MLOps Podcast #369 with Maher Hanafi, SVP of Engineering at Betterworks Key topics covered: The AI Iceberg — Why the invisible work behind AI (performance, latency, throughput, cost, accuracy) is harder than building the features themselves GPU Cost Optimization — How upgrading to more expensive GPUs actually saved money by reducing total runtime hours TensorRT LLM Deep Dive — Rewiring neural networks to match GPU architecture for 50-70% latency reduction Cold Start Solutions — Using AWS FSx, baking models into container images, and cutting minutes off spin-up times KV Cache & In-Flight Batching — Why using one model per GPU with maximum KV cache beats cramming multiple models together Scheduled & Dynamic Scaling — Pattern-based scaling for HR tech workloads (nights, weekends, end-of-quarter spikes) Verticalized AI Platform — Building horizontal AI infrastructure that serves multiple HR product verticals AI Engineering Lab — How junior vs. senior engineers adopted AI coding tools differently, and the cultural shift that followed Agentic Coding in Practice — Navigating AI coding agent costs, quality control, and redefining the SDLC Chinese Models & Compliance — Why enterprise customers block DeepSeek/Qwen and the geopolitics of model training data This episode is for engineering leaders building AI in production, MLOps engineers optimizing GPU infrastructure, and anyone navigating the gap between AI demos and enterprise-scale deployment. Links & Resources: TensorRT LLM: https://github.com/NVIDIA/TensorRT-LLM NVIDIA Run: ai Model Streamer (cold start optimization): https://developer.nvidia.com/blog/reducing-cold-start-latency-for-llm-inference-with-nvidia-runai-model-streamer/ vLLM vs TensorRT-LLM comparison: https://northflank.com/blog/vllm-vs-tensorrt-llm-and-how-to-run-them Timestamps: 0:00 — Intro & teaser clips 1:00 — Maher's journey from traditional engineering to AI leadership 4:30 — The AI iceberg: cost, performance, latency, throughput, accuracy 8:00 — Managing AI coding agent costs & premium token budgets 12:00 — GPU scaling strategies: scheduled, dynamic, and proactive 16:00 — Cold start problem: FSx, baked images, and container optimization 20:00 — TensorRT LLM: 50-70% latency reduction explained 25:00 — KV cache, in-flight batching, and throughput optimization 30:00 — The counterintuitive math: bigger GPUs = lower cost 35:00 — Verticalized AI products for HR tech40:00 — Building a horizontal AI platform with preprocessing layers 45:00 — AI feedback polishing: the feature that needed guardrails 50:00 — AI Engineering Lab: adoption curves by seniority 55:00 — Redefining the SDLC for AI-assisted development 60:00 — Self-hosting coding agents & leveraging internal AI platform 63:00 — Chinese models, compliance, and training data bias
