# AWS Trainium Published articles for AWS Trainium. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Happy 20th Birthday, Amazon EC2 DevFeed: [Happy 20th Birthday, Amazon EC2]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-25T16:11:02Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [Amazon EC2](), [Inference Performance]() Tags: [ai-inference](), [amazon-cloudwatch](), [amazon-ec2](), [amazon-ec2-mac-instances](), [apple](), [aws](), [aws-inferentia](), [aws-local-zones](), [aws-outposts](), [aws-trainium](), [cloud](), [cloud-computing](), [general](), [generative-ai](), [graviton](), [infrastructure](), [intel](), [linux](), [news](), [nitro-system](), [performance]() ### AI overview AWS marks Amazon EC2's 20th anniversary by reviewing its evolution from a single Linux virtual-server instance to a global service with more than 1,200 instance types. The article highlights foundational services, expanded deployment options, and AWS-designed silicon for AI inference and training. ### Source excerpt On the 20th Anniversary, we recognize how AWS has continued to push the boundaries of what cloud computing can deliver, building custom silicon for general-purpose and AI workloads and expanding EC2 into new form factors and deployment models that our customers in 2006 could not have imagined. ## AWS Trainium Frontier competition: Co-design models and kernels on purpose-built AI chips DevFeed: [AWS Trainium Frontier competition: Co-design models and kernels on purpose-built AI chips]() Original publisher: [Read original article]() Author: Louise Ping; John Gray; Emily Webber; Josh Longenecker Published: 2026-08-10T20:23:04Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [AI Chat]() Tags: [aws-trainium](), [chip-design](), [hardware](), [kernels](), [language-models](), [machine-learning](), [model-architecture](), [neurips](), [performance](), [training]() ### AI overview AWS Trainium Frontier is a competition for training language models from scratch on Trainium while co-designing architectures, optimizers, training loops, and optional custom kernels under fixed compute and time budgets. ### Source excerpt A competition with a finalist ceremony during NeurIPS 2026, challenging researchers to train language models from scratch on Trainium, exploring what optimal architectures look like when the hardware changes. ## 34 Amazon Research Awards Build on Trainium recipients announced DevFeed: [34 Amazon Research Awards Build on Trainium recipients announced]() Original publisher: [Read original article]() Author: Amazon Research Awards team Published: 2026-08-05T15:00:00Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [Artificial Intelligence](), [responsible-ai](), [AWS AI chips](), [AI Research](), [Optimization](), [Inference](), [moe](), [Large Language Model](), [distributed-systems](), [synthetic-data]() Tags: [academic-ai-funding](), [ai](), [ai-research](), [ai-research-grants](), [ai-safety-and-alignment](), [amazon-research-awards](), [ara](), [aws-ai-chips](), [aws-trainium](), [build-on-trainium](), [data](), [deep-learning](), [distributed-systems](), [inference](), [internal-ara-program-updates](), [llm](), [machine-learning-research](), [mixture-of-experts](), [optimization](), [research](), [responsible-ai](), [synthetic](), [synthetic-data-generation]() ### AI overview Amazon announces 34 recipients of its Build on Trainium program, a $110 million credit initiative supporting AI research and university education. The awards fund work in areas including Responsible AI, language models, synthetic data, distributed systems, model architectures, libraries, and optimization on AWS Trainium. ### Source excerpt Amazon announces 34 recipients of the Build on Trainium program, a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI. ## Deploy models on AWS Inferentia2 from Hugging Face DevFeed: [Deploy models on AWS Inferentia2 from Hugging Face]() Original publisher: [Read original article]() Author: Jeff Boudier; Philipp Schmid Published: 2024-05-22T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [AWS AI chips](), [inference-endpoints](), [hugging face](), [Amazon SageMaker](), [Large Language Model](), [tgi](), [autoscaling](), [Amazon Web Services](), [MLOps](), [Open Source]() Tags: [autoscaling](), [aws](), [aws-trainium](), [cloud](), [deployment](), [hugging-face](), [inference](), [inference-endpoints](), [large-language-models-llms](), [llama3](), [mlops](), [model](), [optimum](), [partnership](), [tgi]() ### AI overview Hugging Face announces support for deploying models on AWS Inferentia2 through Amazon SageMaker and Hugging Face Inference Endpoints. The update enables scalable inference for supported models, including Meta Llama 3, with multiple instance sizes, managed features, and autoscaling. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.