AI Systems Performance Engineering

Chapter 1. Introduction and AI System Overview

In late 2024, a small startup in China called DeepSeek.AI stunned the AI community by training a frontier large language model (LLM) without access to the latest, state-of-the-art NVIDIA GPUs at the time. Due to export restrictions, DeepSeek’s engineers could not obtain top-tier NVIDIA Blackwell (B200, B300, etc.) or Hopper (H100, H200, etc.) GPUs, so they resorted to locally available, export-compliant alternatives at the time, including the NVIDIA H800 GPU. They used custom kernels and advanced optimization techniques such as model distillation to squeeze out maximum performance from these less capable GPUs.

Despite these limitations, DeepSeek.AI trained their DeepSeek-R1 model and achieved reasoning capabilities near the performance of leading frontier models that were trained on the most capable NVIDIA chips at the time. This case underscores that practitioners and researchers skilled in AI systems performance engineering can get the most out of their available hardware—no matter the constraints.

For example, DeepSeek’s engineers treated communication bandwidth as a scarce resource, optimizing every byte over the wire to achieve what many thought impossible on that infrastructure. They scaled out to thousands of these constrained GPUs—connected with limited-bandwidth interconnects—using novel software and algorithmic optimizations to overcome these limitations.

Contrast DeepSeek’s approach with the “brute force” path taken by ...