NVIDIA and AWS Deepen Partnership, G7 Instances Deliver 4.6x Inference Performance Boost
Alina Collins
NVIDIA and AWS announced a three-layer AI infrastructure upgrade on June 23 — inference compute, vector search, and large-scale training — aimed at moving enterprise AI projects into production faster and cheaper.
What makes the new G7 instances a big deal?
AWS launched EC2 G7 instances powered by NVIDIA's RTX PRO 4500 Blackwell Server Edition GPUs. Compared with the previous G6, G7 delivers up to 4.6× AI inference performance and up to 2.1× graphics performance.
This means → the same cloud bill buys several times more inference throughput — a direct cost lever for companies moving AI from the lab into live products.
Each G7 instance supports up to 8 GPUs, 256 GB total GPU memory, 700 Gbps EFA network bandwidth, and 7.6 TB local NVMe SSD storage, with 1/2/4/8-GPU configurations available and bare-metal support coming soon.
A billion-scale vector database built in under an hour?
AWS made GPU-accelerated vector indexing the default option for Amazon OpenSearch Serverless, powered by NVIDIA cuVS — a GPU library purpose-built for vector search.
Compared with CPU-only approaches, vector indexing is up to 10× faster at one-quarter the cost. A billion-scale vector database can be built within one hour.
In plain terms = for agentic AI, RAG — retrieval-augmented generation, where a model looks up information before answering — and semantic search, the slowest step (building the index) just got dramatically shorter, and it's on by default with no extra setup.
What does "Exemplar Cloud" certification actually mean?
On the training side, AWS earned NVIDIA's "Exemplar Cloud" certification for GB300.
This means → NVIDIA ran its own reference-architecture benchmarks and confirmed that AWS infrastructure meets the performance bar — effectively issuing a "performance certificate" for large-scale training workloads.
For enterprise customers, this is a quantifiable performance guarantee: large-model training on AWS will not fall below NVIDIA's published baseline.
Three layers at once — what does it mean for enterprises?
Inference (G7 instances) + retrieval (cuVS acceleration) + training (Exemplar Cloud certification) landing simultaneously means enterprises building AI on AWS now have NVIDIA acceleration across the full pipeline — from data retrieval to model inference to model training.
This reflects a shift in how cloud providers and chip makers partner: from "selling individual GPUs" to "selling a complete AI production line" — locking enterprises into one platform for the entire workflow.
The real uncertainty is G7's actual pricing and cuVS's stability in production environments. The performance numbers look strong, but enterprises ultimately run a total-cost calculation.
Content is for reference only, not financial advice.