AI is transferring quick, and for many of our prospects, the true alternative isn’t in experimenting with it—it’s in operating AI in manufacturing the place it drives significant enterprise outcomes. This implies constructing techniques that run reliably, carry out at scale, and meet your group’s safety and compliance necessities.
Right now at NVIDIA GTC 2026, AWS and NVIDIA introduced an expanded collaboration with new know-how integrations to assist rising AI compute demand and assist you construct and run AI options which are production-ready. These integrations span accelerated computing, interconnect applied sciences, and mannequin fine-tuning and inference. They embrace:
Main bulletins at NVIDIA GTC 2026
Scaling AI infrastructure with expanded GPU choices and optimized interconnect
Accelerating compute capability within the agentic AI period
Beginning in 2026, AWS will add greater than 1 million NVIDIA GPUs together with Blackwell and Rubin GPU architectures throughout our world cloud areas. AWS presents the broadest assortment of NVIDIA GPU-based cases of any cloud supplier to energy a various set of AI/ML workloads. AWS and NVIDIA are additionally collaborating on Spectrum networking and different infrastructure areas, including to over 15 years of joint innovation between our two corporations.
AWS’s superior cloud and AI infrastructure supplies enterprises, startups, and researchers with the infrastructure wanted to construct and scale agentic AI techniques—able to reasoning, planning, and appearing autonomously throughout complicated workflows.
New Amazon EC2 cases with NVIDIA RTX PRO 4500 Blackwell Server Version GPUs
Right now, we introduced that Amazon EC2 cases accelerated by NVIDIA RTX PRO 4500 Blackwell Server Version GPUs are coming quickly. AWS is the primary main cloud supplier to announce assist for RTX PRO 4500 Blackwell Server Version GPUs. These cases are well-suited for a variety of workloads, together with knowledge analytics, conversational AI, content material technology, recommender techniques, video streaming, video rendering, and different graphics workloads.
Amazon EC2 cases accelerated by NVIDIA RTX PRO 4500 Blackwell Server Version GPUs will likely be constructed on the AWS Nitro System, a mixture of devoted {hardware} and light-weight hypervisor which delivers virtually the entire compute and reminiscence assets of the host {hardware} to your cases for higher total useful resource utilization and efficiency. The Nitro System’s specialised {hardware}, software program, and firmware are designed to implement restrictions in order that no person, together with anybody at AWS, can entry your delicate AI workloads and knowledge. As well as, the Nitro System helps firmware updates, bug fixes, and optimizations whereas the system stays operational. These capabilities inside the Nitro System allow the improved useful resource effectivity, safety, and stability that AI, analytics, and graphics workloads require in manufacturing.
Accelerating interconnect for disaggregated LLM inference with NVIDIA NIXL on AWS EFA and Trainium
As mannequin sizes develop, communication overhead between GPUs or Trainium can turn into a bottleneck. Right now, we introduced assist for NVIDIA Inference Xfer Library (NIXL) with AWS EFA to speed up disaggregated Giant Language Mannequin (LLM) inference on Amazon EC2, throughout NVIDIA GPUs and AWS Trainiums. Accelerating disaggregated inference is essential for scaling trendy AI workloads as a result of it permits environment friendly overlap of communication and computation whereas minimizing communication latency and maximizing GPU utilization. This integration permits high-throughput, low-latency KV-cache knowledge motion between GPU compute nodes performing token technology and distributed reminiscence assets that retailer KV-cache state. It additionally supplies the flexibleness to construct inference clusters utilizing any mixture of GPU and Trainium EFA-enabled EC2 cases. NIXL with EFA integrates natively with fashionable open-source frameworks similar to NVIDIA Dynamo, vLLM, and SGLang, delivering improved inter-token latency and extra environment friendly KV-cache reminiscence utilization.
Accelerating knowledge analytics with Amazon EMR and NVIDIA GPUs
Working Apache Spark 3x sooner utilizing Amazon EMR on Amazon EKS with G7e cases
Knowledge engineers and knowledge scientists regularly face hours-long knowledge processing pipelines that gradual AI/ML mannequin iteration and enterprise intelligence technology. We’re seeing vital efficiency positive aspects for these workloads—AWS and NVIDIA ship 3x sooner efficiency for Apache Spark workloads with Amazon EMR on EKS on G7e cases. This efficiency outcomes from joint AWS-NVIDIA engineering collaboration optimizing GPU-accelerated analytics by combining Amazon EMR on EKS with NVIDIA’s RTX PRO 6000 structure. With Amazon EMR and G7e cases, knowledge engineers and knowledge scientists can speed up time-to-insight for AI/ML characteristic engineering, complicated ETL transformations, and real-time analytics at scale. Prospects operating large-scale knowledge processing pipelines can minimize the time wanted to run analytics whereas sustaining full compatibility with current Spark functions.
Increasing NVIDIA Nemotron mannequin assist on Amazon Bedrock
Effective-tuning Nemotron fashions in Amazon Bedrock with Reinforcement Effective-Tuning (Coming quickly)
Builders will quickly be capable of fine-tune NVIDIA Nemotron fashions immediately on Amazon Bedrock utilizing Reinforcement Effective-Tuning (RFT). That is vital for groups that have to align mannequin habits to particular domains, whether or not that’s authorized, healthcare, finance, or some other specialised discipline. Reinforcement fine-tuning allows you to form how a mannequin causes and responds, not simply what it is aware of. And since this runs natively on Amazon Bedrock, there’s zero infrastructure overhead. You outline the duty, present the suggestions sign, and Bedrock handles the remainder. Find out about Reinforcement Effective-Tuning in Amazon Bedrock.
Nemotron 3 Tremendous on Amazon Bedrock (Coming quickly)
NVIDIA Nemotron 3 Tremendous—a hybrid MoE mannequin constructed for multi-agent workloads and prolonged reasoning—is coming quickly to Amazon Bedrock. Designed to allow AI brokers to take care of accuracy throughout complicated, multi-step workflows, it powers use instances throughout finance cybersecurity, retail , and software program improvement—delivering quick, cost-efficient inference by way of a totally managed API.
Bettering vitality effectivity and sustainability
As AI workloads scale, efficiency per watt isn’t only a sustainability metric—it’s a aggressive benefit. On this NVIDIA GTC session, Amazon CSO Kara Hurst will be a part of sustainability leaders from Equinix and PepsiCo to debate how AI is remodeling enterprise vitality and infrastructure at scale—from knowledge facilities as energetic grid individuals to AI as an enterprise effectivity engine, and the way AWS may help you obtain optimum vitality effectivity with AWS infrastructure being 4.1x extra energy-efficient than on-premises knowledge facilities.
Constructed to run, collectively
What makes these bulletins thrilling isn’t any single functionality—it’s what they characterize collectively. Fifteen years of partnership between AWS and NVIDIA has produced a full stack of AI infrastructure optimized finish to finish, from the GPU to the community to the managed companies layer. You don’t should sew it collectively yourselves. It’s able to run.
For those who’re at GTC this week, come discover us on the AWS sales space. Take a look at dwell demos, catch our in-booth theater classes, and decide up custom-made swag with AWS Swag Manufacturing unit.
Go to AWS at NVIDIA GTC 2026 to see the whole lot AWS has occurring on the convention.
In regards to the authors
David Brown
David Brown is the Vice President of AWS Compute and Machine Studying (ML) Providers. On this position he’s answerable for constructing all AWS Compute and ML companies, together with Amazon EC2, Amazon Container Providers, AWS Lambda, Amazon Bedrock and Amazon SageMaker. These companies are utilized by all AWS prospects but additionally underpin most of AWS’s inside Amazon functions. He additionally leads newer options, similar to AWS Outposts, that carry AWS companies into prospects’ personal knowledge facilities.
