Sandia National Laboratories has joined a new partnership aimed at helping the United States regain its leadership in semiconductor manufacturing. While the U.S. was considered a powerhouse in chip production in the 1990s, fabricating more than 35% of the world’s semiconductors, that share has since dropped to 12%. Today, theContinue Reading
One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already small-scale quantum computers can boost the performance of machine learning algorithms. This was demonstrated on a photonic quantum processor by anContinue Reading
The exponential miniaturization of electronic chips over time, described by Moore’s law, has played a key role in our digital age. However, the operating power of small electronic devices is significantly limited by the lack of advanced cooling technologies available. Aiming to tackle this problem, a study published in CellContinue Reading
Plasma — the electrically charged fourth state of matter — is at the heart of many important industrial processes, including those used to make computer chips and coat materials. Simulating those plasmas can be challenging, however, because millions of math operations must be performed for thousands of points in theContinue Reading
Large language models (LLMs) have revolutionized the way we interact with technology, but their widespread adoption has been blocked by high inference latency, limited throughput, and high costs associated with text generation. These inefficiencies are particularly pronounced during high-demand events like Amazon Prime Day, where systems like Rufus—the Amazon AI-poweredContinue Reading
The Qwen 2.5 multilingual large language models (LLMs) are a collection of pre-trained and instruction tuned generative models in 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B (text in/text out and code out). The Qwen 2.5 fine tuned text-only models are optimized for multilingual dialogue use cases and outperform both previousContinue Reading
The use of large language models (LLMs) and generative AI has exploded over the last year. With the release of powerful publicly available foundation models, tools for training, fine tuning and hosting your own LLM have also become democratized. Using vLLM on AWS Trainium and Inferentia makes it possible toContinue Reading
Many organizations are building generative AI applications powered by large language models (LLMs) to boost productivity and build differentiated experiences. These LLMs are large and complex and deploying them requires powerful computing resources and results in high inference costs. For businesses and researchers with limited resources, the high inference costsContinue Reading
Amazon Rufus is a shopping assistant experience powered by generative AI. It generates answers using relevant information from across Amazon and the web to help Amazon customers make better, more informed shopping decisions. With Rufus, customers can shop alongside a generative AI-powered expert that knows Amazon’s selection inside and out,Continue Reading
Today, we are excited to announce AWS Trainium and AWS Inferentia support for fine-tuning and inference of the Llama 3.1 models. The Llama 3.1 family of multilingual large language models (LLMs) is a collection of pre-trained and instruction tuned generative models in 8B, 70B, and 405B sizes. In a previousContinue Reading
