We’re proud to announce that Cerebras has been recognized as a Gold winner in The Stevie® Awards 22nd Annual American Business Awards® for Most Innovative Tech Company of the Year!

“Cerebras Systems' groundbreaking achievements in AI technology, demonstrated by the innovative

🚀 Transforming Enterprise AI at Scale 🚀

'Scale sets a speed limit; if you don't have scale, you can't train some of these massive models.' In a recent interview with Sequoia, Andrej Karpathy mentions this and highlights the difficulty with instrumenting large GPU clusters that

In Eric Savitz' latest article for Barron's, Cerebras is described as the most intriguing startup that is building AI supercomputers that rival NVIDIA.

Highlights from the article:

🔥 Cerebras Wafer Scale Engine 3 (WSE-3) is 72 square inches and is the largest commercial chip

Cerebras VP and Field CTO, Natalia Vassilieva, speaks at Weights & Biases Fully Connected Conference. Natalia will discuss training recipes and scaling strategies for high-quality enterprise GenAI Models.

📅 April 18th, 2024
🕒 3:50 PM - 4:15 PM PDT
🎟️ Registration details -

Cerebras Strategist Michelle Fang will be speaking at the Forging the Future of Business with AI Conference, hosted by Forbes, Imagination in Action, and MIT Connection Science.

Michelle will speak at a panel discussion on “GenAI for Innovation,' which will unpack emerging best

Congratulations to our strategic partner, G42, on their partnership and $1.5B investment from Microsoft.

This marks a significant milestone in G42’s journey as a global tech leader and accelerates their world-changing vision to accelerate AI development and global expansion.

TSMC thinks it will take the rest of the industry 6 more years to achieve ¼ of the transistors that we already have today.

Would you like to enjoy 4 trillion transistors today on CS-3 or 1-trillion transistors on a GPU in 2030?

Read TSMC’s full report (spectrum.ieee.org/trillion-trans…)

In the fast-paced world of AI hardware, the Cerebras CS-3 and Nvidia DGX B200 are two of the most exciting new offerings to hit the market in 2024. Both systems are designed to tackle large-scale AI training, but they take decidedly different approaches.

For ML researchers and

🌟 Cerebras is thrilled to be selected on the 2024 Forbes AI 50! 🌟

Here are a few reasons why we made the cut:

🎉 Cerebras is the only AI chip startup on this year’s list. Learn more about our latest generation of hardware, the CS-3: cerebras.net/product-system/

🎉 We enable top

The Cerebras CS-3 is the world's most scalable AI supercomputer. From 125 petaflops to 256 exaflops, 1 terabyte of memory to 1 petabyte, it provides the fastest and simplest way to achieve high model utilization on supercomputing hardware.

🌟 Our March Newsletter is here! 🌟

Check out the following information:
🚀 Learn about our announcements leading up to Cerebras AI Day
📺 Watch our keynotes, panelist discussions, and fireside chats
📖 Read a few articles published about Cerebras

Enjoy!

Cerebras is proud to build and operate the world’s largest AI #supercomputers here in the USA!

Condor Galaxy, built with our strategic partner G42, is among the largest #AI supercomputing installations in the world.

The first two clusters are based in Santa Clara and

8 Exaflops, 64 systems, 108TB of memory – say hello to the Condor Galaxy 3 (CG-3) – the first AI #supercomputer powered by the Cerebras CS-3, built with our strategic partner G42.

CG-3 provides the highest density of AI compute for our next generation of #AI builders. Online Q2

The secret of Cerebras’ architecture isn’t just our giant wafer – it’s our highly scalable wafer-scale cluster design.

This means that whether you program 1 or 2048 nodes, the entire cluster appears as a single chip. No Megatron, no DeepSpeed, no sharding – it’s the speed of a

The Cerebras CS-3 redefines scalability in AI supercomputing.

A 2048 CS-3 cluster can deliver an astounding 256 exaflops of AI compute. This makes it possible to train Llama2-70B in less than one day—a task that would take at least one month on gigantic GPU clusters.

The entire