March 28, 2023
Researchers looking to create a foundation for a ChatGPT-style application now have an affordable way to do so. Cerebras is releasing open-source learning models for researchers with the ingredients necessary to cook up their own ChatGPT-AI applications. The open-source tools include seven models that form a learning... Read more…
February 28, 2023
Since ChatGPT took the world by storm, companies opened pocketbooks to explore the tech. It also brought attention to OpenAI's GPT-3, the large-language model b Read more…
November 17, 2022
For three years running, ACM has awarded not only its long-standing Gordon Bell Prize (read more about this year’s winner here!) but also its Gordon Bell Spec Read more…
November 17, 2022
Large language models (LLMs) have taken the tech world by storm over the past couple of years, dominating headlines with their ability to generate convincing hu Read more…
November 14, 2022
Cerebras is putting down stakes to be a player in the AI cloud computing with a supercomputer called Andromeda, which achieves over an exaflops of "AI performan Read more…
October 19, 2022
Cerebras Systems has secured another U.S. government win for its wafer scale engine chip – which is considered the largest chip in the world. The company's chip technology will be part of a research project sponsored by the National Nuclear Security Administration to find... Read more…
August 3, 2022
When Cerebras Systems had its coming out at Hot Chips in August 2019, the hardware community wasn't sure what to think. Attendees were understandably skeptical of the novel "wafer-scale" technology, not to mention an estimated power envelope of ~15 kilowatts for the chip alone. In the intervening three years, the company... Read more…
May 25, 2022
The battle among high-performance computing hubs to stack up on cutting-edge computers for quicker time to science is getting steamy as new chip technologies become mainstream. A European supercomputing hub near Munich, called the Leibniz Supercomputing Centre, is deploying Cerebras Systems' CS-2 AI system as part of an internal initiative called Future Computing to assess alternative computing... Read more…
The increasing complexity of electric vehicles result in large and complex computational models for simulations that demand enormous compute resources. On-premises high-performance computing (HPC) clusters and computer-aided engineering (CAE) tools are commonly used but some limitations occur when the models are too big or when multiple iterations need to be done in a very short term, leading to a lack of available compute resources. In this hybrid approach, cloud computing offers a flexible and cost-effective alternative, allowing engineers to utilize the latest hardware and software on-demand. Ansys Gateway powered by AWS, a cloud-based simulation software platform, drives efficiencies in automotive engineering simulations. Complete Ansys simulation and CAE/CAD developments can be managed in the cloud with access to AWS’s latest hardware instances, providing significant runtime acceleration.
Two recent studies show how Ansys Gateway powered by AWS can balance run times and costs, making it a compelling solution for automotive development.
Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
To ensure your AI systems are optimized, follow these five essential steps to eliminate bottlenecks and maximize efficiency.
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