Zhihan Jiang

Zhihan Jiang is an engineer manager in the TensorRT team at NVIDIA and focuses on delivering world-class generative AI inference results in MLPerf Inference. Before working on MLPerf, he worked on TensorRT autonomous safety libraries and infrastructure, and NVIDIA CPU architecture modeling. Zhihan holds a master’s degree in Electrical Engineering from Stanford University, and a bachelor’s degree in Computer Engineering from Georgia Tech.

Posts by Zhihan Jiang

Data Center / Cloud Sep 09, 2025

NVIDIA Blackwell Ultra Sets New Inference Records in MLPerf Debut

As large language models (LLMs) grow larger, they get smarter, with open models from leading developers now featuring hundreds of billions of parameters. At the... 9 MIN READ

Data Center / Cloud Apr 02, 2025

NVIDIA Blackwell Delivers Massive Performance Leaps in MLPerf Inference v5.0

The compute demands for large language model (LLM) inference are growing rapidly, fueled by the combination of growing model sizes, real-time latency... 10 MIN READ

Data Center / Cloud Aug 28, 2024

NVIDIA Blackwell Platform Sets New LLM Inference Records in MLPerf Inference v4.1

Large language model (LLM) inference is a full-stack challenge. Powerful GPUs, high-bandwidth GPU-to-GPU interconnects, efficient acceleration libraries, and a... 13 MIN READ

An image of an NVIDIA H200 Tensor Core GPU.

Generative AI Mar 27, 2024

NVIDIA H200 Tensor Core GPUs and NVIDIA TensorRT-LLM Set MLPerf LLM Inference Records

Generative AI is unlocking new computing applications that greatly augment human capability, enabled by continued model innovation. Generative AI... 11 MIN READ

Data Center / Cloud Sep 09, 2023

Leading MLPerf Inference v3.1 Results with NVIDIA GH200 Grace Hopper Superchip Debut

AI is transforming computing, and inference is how the capabilities of AI are deployed in the world’s applications. Intelligent chatbots, image and video... 13 MIN READ

Data Center / Cloud Apr 05, 2023

Setting New Records in MLPerf Inference v3.0 with Full-Stack Optimizations for AI

The most exciting computing applications currently rely on training and running inference on complex AI models, often in demanding, real-time deployment... 15 MIN READ