“With each successive generation of chip, AWS delivers better price performance and energy efficiency, giving customers even more options-in addition to chip/instance combinations featuring the latest chips from third parties like AMD, Intel, and NVIDIA-to run virtually any application or workload on Amazon Elastic Compute Cloud (Amazon EC2).”Īmazon did not specify the release date for Trainium2 instances to AWS customers, except for indicating they will be available “sometime next year.” The second chip introduced was Graviton4. The cost-effective Trn2 instances aim to accelerate advances in generative AI by delivering high-scale ML training performance,” Amazon said in a press release. This scale enables customers to train large language models with 300 billion parameters in weeks rather than months. “Trainium2 chips are designed for high-performance training of models with trillions of parameters. (“Exaflops” and “teraflops” measure how many floating-point operations a chip can perform per second.) This power will enable AWS customers to train large language models with 300 billion parameters in weeks rather than months.ĪWS Graviton4 and AWS Trainium2 processors. The company said this provides supercomputer-class performance with up to 65 exaflops of compute power. Trainium2 is scalable and can reach deployments of up to 100,000 chips in AWS’ EC2 UltraCluster product. Amazon plans to make it accessible in EC Trn2 instances, organized in clusters of 16 chips. Trainium2 & Graviton4: AI model training chipsĪmazon took the stage at the re:Invent conference to introduce its latest chip generation for model training and inferencing, combatting the growing demand for generative AI on GPUs, with Nvidia’s offerings in short supply.ĪWS’s first chip, AWS Trainium2, aims to provide up to four times improved performance and two times better energy efficiency than its predecessor, Trainium, introduced in December 2020. Adam Selipsky, AWS CEO, during his keynote address, sharing about innovations in data, infrastructure, & AI/ML.
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