3-Part Series: LLM Latency in Production (Part 1) | Towards AI

Author(s): Mehedi Hasan Originally published on Towards AI. 3-Part Series: LLM Latency in Production (Part 1)Originally published at https://mhabir.substack ...

mercoledì 3 giugno 2026 New tab

TL;DRAI

LLM decode is memory-bandwidth bound; quantization (INT8/INT4/AWQ) and optimized kernels (Flash Attention, Paged Attention) deliver 3–4x latency improvement. Teams shipping production models waste weeks tuning ineffective batching before discovering this baseline fix—critical for cost and throughput optimization.

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Last Updated on June 3, 2026 by Editorial Team

Originally published on Towards AI.

Originally published at https://mhabir.substack.com.

If you’re shipping LLMs to production, your first performance bottleneck isn’t serving logic or network overhead-it’s the raw arithmetic happening inside the GPU. Most teams waste weeks tuning their batching logic before realizing their model baseline is 3–4x slower than it should be. This part is about fixing that baseline.

3-Part Series: LLM Latency in Production (Part 1) | Towards AI

3-Part Series: LLM Latency in Production (Part 1) | Towards AI

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Other newsrooms on this story

Related reading

LLM Speed Benchmarks: Metrics & Infrastructure Guide

Stop Using LLMs to Audit Other LLMs: You Are Bricking Your Production Latency

Stop paying for idle GPUs in your CI: batching LLM eval jobs

Notes on Serving LLMs with TensorRT-LLM and Triton

Maximizing GPU Utilization with NVIDIA Run:ai and NVIDIA NIM | NVIDIA Technical…

Unweight: how we compressed an LLM 22% without sacrificing quality