Choosing models by pre-fill vs decoding needs

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:

[00:00] Optimizing GPU Usage and Latency

[00:21] Learning AI as Leadership

[04:34] AI Cost Centers

[13:56] Throughput and Infrastructure Efficiency

[18:10] Scaling and Unit Economics

[24:14] Championing AI ROI

[36:11] Queue to Value Engine

[41:30] Failed Product Features

[46:12] Agentic Engineering Costs

[58:49] AI Self-Hosting in Engineering

[1:04:40] Wrap up