Agentic AI · Blackwell · Nvidia · Amazon · NVIDIA Blog
Building AI systems at scale is demanding, requiring low-latency inference, fast vector search
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NVIDIA’s latest work with Amazon Web Services (AWS) addresses each of those constraints.
Key facts
- 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
- EC2 G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs expand the compute layer for AI, graphics, video and data analytics workloads, while the NVIDIA cuVS library accelerates
- G7 instances are accessible through AWS Deep Learning Amazon Machine Images (AMIs), Amazon Deep Learning Containers, Amazon EMR, Amazon EKS, Amazon ECS and graphics AMIs, and coming soon to Amazon
- AWS has achieved NVIDIA Exemplar Cloud status on NVIDIA GB300 for training workloads
Summary
Building AI systems at scale is demanding, requiring low-latency inference, fast vector search, strong GPU price-performance and infrastructure that can grow without multiplying operational complexity. EC2 G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs expand the compute layer for AI, graphics, video and data analytics workloads, while the NVIDIA cuVS library accelerates the retrieval layer by making GPU-powered vector indexing the default in OpenSearch Serverless. 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.