NVIDIA RTX PRO 4500 Blackwell GPU powering high-performance Amazon EC2 G7 instances for accelerated computing and AI workload

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NVIDIA RTX PRO 4500 Blackwell GPUs Power New Amazon EC2...

NVIDIA RTX PRO 4500 Blackwell GPUs Power New Amazon EC2 G7 Instances

By AI Daily Post Edited by Brian Petersen, Editor-in-Chief

June 24, 2026 • 3 min read

Here's the thing: Amazon EC2’s new G7 instances ship with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, positioning the cloud for a range of production workloads that need raw GPU power without the hassle of managing hardware yourself. While the tech is impressive, the numbers speak louder than hype—G7 promises up to 4.6× the AI inference performance and up to 2.1× the graphics throughput of its G6 predecessor, plus noticeably faster GPU‑accelerated analytics on Amazon EMR when using NVIDIA’s cuDF library for Spark. But here's the reality: the offering isn’t a one‑size‑fits‑all.

Customers can choose from one‑, two‑, four‑ or eight‑GPU configurations, each supporting up to 256 GB of total GPU memory, 700 Gbps of EFA‑enabled networking, and as much as 7.6 TB of local NVMe SSD storage; bare‑metal variants are slated to arrive soon. The versatility means AI teams see lower‑latency inference, media groups run high‑resolution video pipelines, and data engineers tap the expanded memory and storage for analytics or vector‑database workloads. Access comes through Deep Learning AMIs, containers, EMR, EKS, ECS, graphics AMIs—and SageMaker support is on the horizon.

NVIDIA RTX PRO 4500 Blackwell Server Edition Multi-Workload GPUs Power New Amazon EC2 G7 Instances
Amazon EC2 G7 instances bring NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs to AWS for AI inference, graphics, spatial computing and GPU-accelerated data analytics -- delivering a new instance type engineered for production workloads that need performance without the operational overhead of a customer-managed GPU platform.
Compared with G6 instances, G7 delivers up to 4.6x AI inference performance, up to 2.1x graphics performance and significantly faster GPU-accelerated data analytics on Amazon EMR using the NVIDIA cuDF library for Apache Spark workloads.
With support for up to eight GPUs, 256GB of total GPU memory, 700 Gbps of EFA-enabled networking and up to 7.6TB of local NVMe SSD storage -- across one-, two-, four- and eight- GPU configurations plus bare metal, coming soon -- G7 instances let customers right-size infrastructure for their workloads instead of over-provisioning for them.
The platform's versatility means AI teams get lower-latency inference.

NVIDIA and AWS Collaborate to Bring AI to Production at Scale - NVIDIA AI Blog

Why this matters

Developers now have a ready‑made EC2 G7 option that pairs NVIDIA’s RTX PRO 4500 Blackwell Server Edition GPUs with AWS’s managed infrastructure, promising AI inference, graphics, spatial computing and data‑analytics workloads without the need to spin up and maintain their own GPU clusters. But the real test will be whether the performance gains justify the pricing model for production‑scale deployments. If the instances truly deliver “performance without the operational overhead,” startups could redirect engineering resources toward model development rather than hardware ops.

Conversely, firms accustomed to fine‑tuning on‑prem GPUs may find the abstraction limiting, especially if custom driver tweaks are required. We appreciate the convenience of a multi‑workload GPU that Amazon markets as production‑ready, yet we remain cautious about the long‑term cost efficiency and flexibility compared to self‑managed solutions. Our takeaway: the G7 instances expand the toolbox for AI teams, but careful benchmarking will be essential before committing critical pipelines to this new offering.