AWS shipped three things this week that matter for any organisation building or buying AI tools: new faster servers, cheaper AI search, and a performance certification for large training jobs. None of it is flashy. All of it is real.
Start with the servers. AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, and has made these new machines, called EC2 G7 instances, generally available. They deliver up to 4.6 times the AI processing performance and up to 2.1 times the graphics performance compared to the previous generation. That is a meaningful jump, not a rounding error.
The G7 machines are also more versatile than what came before. Instead of needing separate server types for graphics and AI processing, the RTX PRO 4500 handles both effectively. For IT teams managing cloud spending, consolidating workloads onto fewer machine types usually means lower bills and less operational overhead. The networking speed on G7 instances is 7 times faster than the previous generation, at 700 Gbps, which matters for AI applications that need to move large amounts of data quickly.
The new instances are available today in two AWS regions: US East (Ohio) and US West (Oregon). Global rollout has not been confirmed yet, which means organisations outside the US will need to wait.
Now the more broadly relevant update: vector search. This requires a brief explanation. When an AI tool answers a question using your company's own data, it does not read every document. It searches a special index that maps text and data to numbers, then finds the closest matches. This is called vector search, and it is the plumbing behind most AI assistants, recommendation engines, and smart customer-facing tools. AWS announced GPU-powered acceleration for this type of search inside its OpenSearch Service, allowing companies to build very large search indexes in under an hour and to index data up to 10 times faster at a quarter of the cost compared to the old approach.
Before this change, GPU-powered vector search was a specialist project requiring extra configuration. AWS has now made GPU-powered vector indexing the default for all new vector collections, meaning teams building AI applications get faster search without treating it as a separate optimisation task. Companies do not need to set up or manage the GPU hardware themselves, and they do not pay for it when it is idle.
For business operators, the cost angle is the one to focus on. The vector database market has grown from $1.73 billion in 2024 to a projected $10.6 billion by 2032, which tells you something about how central this infrastructure has become. Any reduction in the cost of running it has a direct effect on the economics of AI tools built on top of it.
The third piece is a certification called NVIDIA Exemplar Cloud status. The NVIDIA Exemplar Clouds initiative evaluates cloud providers against real-world AI training workloads using NVIDIA's performance benchmarking recipes. Achieving this status requires demonstrating more than 95% of the baseline performance across all workloads in a given test suite. For companies running large AI training jobs on AWS, this is a signal that the infrastructure has been independently stress-tested, not just benchmarked on paper. It helps teams evaluate cloud providers with greater confidence and move AI projects from planning to production more efficiently.
The honest read on all three announcements: this is AWS and NVIDIA tightening their partnership and making cloud AI infrastructure more accessible to organisations that are not hyperscale tech companies. Faster search at lower cost, more capable servers, and verified performance benchmarks all point in the same direction. The gap between experimenting with AI and running it in production just got a little smaller.