AI Factories at Scale

Migrating your AI workloads from CPUs to modern GPUs can slash your energy costs by up to 98%.

#1about 2 minutes

The history and origins of the AI company Amber

Amber's journey began in 2006 as Fluid Dyna, an early Nvidia partner for GPU-accelerated code, before being acquired by Altair and later re-established as an independent AI infrastructure company.

#2about 3 minutes

Key milestones in the evolution of AI and GPU computing

The release of CUDA, AlexNet, and Transformers led to an exponential increase in compute demand, culminating in the public adoption of AI with ChatGPT.

#3about 2 minutes

Understanding the business impact and adoption of generative AI

Generative AI presents a massive business opportunity with a high return on investment, driving rapid adoption across major enterprises.

#4about 1 minute

Comparing supercomputer hardware from the past decade

A modern Nvidia DGX H100 system vastly outperforms a state-of-the-art supercomputer from a decade ago while consuming only a fraction of the power and space.

#5about 2 minutes

Why modern GPUs are more energy efficient than CPUs

Replacing legacy CPU-based systems with modern GPUs can reduce energy consumption by up to 98%, and newer GPU generations like Blackwell offer a 4x power reduction over previous models for the same task.

#6about 2 minutes

The shift to production will cause an explosion in compute demand

As generative AI moves from experimentation to production, the demand for compute resources is expected to increase by at least 8 to 10 times, driven primarily by inference workloads.

#7about 3 minutes

Building an AI factory with all the essential components

A successful AI factory requires more than just GPUs; it needs a holistic approach including specialized storage, high-speed networking, management software, and robust data center infrastructure.

#8about 5 minutes

Key software considerations for managing an AI cluster

Effective AI cluster management requires software for optimizing the stack, synchronizing images, monitoring health and performance, integrating with the cloud, and providing chargeback reporting.

#9about 1 minute

Why specialized high-performance storage is critical for AI

AI workloads demand specialized, high-performance storage to handle tasks like rapid LLM checkpointing and high I/O for inference, making legacy storage solutions inadequate.

#10about 3 minutes

Future trends in AI models and data center cooling

The future of AI involves both small specialized models and large general models, driving a necessary evolution in data centers towards direct liquid and immersion cooling to manage heat.