# Curated Digest: Accelerating Generative AI Inference with AWS G7e Instances > Coverage of aws-ml-blog **Published:** April 20, 2026 **Author:** PSEEDR Editorial **Category:** stack **Tags:** AWS, Generative AI, Amazon SageMaker, NVIDIA Blackwell, Cloud Infrastructure, LLM Inference **Canonical URL:** https://pseedr.com/stack/curated-digest-accelerating-generative-ai-inference-with-aws-g7e-instances --- A recent aws-ml-blog post highlights AWS's introduction of G7e instances powered by NVIDIA Blackwell GPUs to Amazon SageMaker AI, offering massive memory and bandwidth upgrades for cost-effective LLM inference. In a recent post, the aws-ml-blog announced the availability of G7e instances for Amazon SageMaker AI, marking a significant hardware upgrade for generative AI workloads. As foundation models and Large Language Models (LLMs) continue to scale in parameter size, the infrastructure required to serve them efficiently has become a primary concern for engineering teams. Inference is notoriously memory-bound; serving massive models requires high memory capacity to store model weights and the KV cache, alongside massive memory bandwidth to generate tokens quickly. Without specialized hardware, organizations face escalating costs, complex multi-node orchestration, and severe performance bottlenecks. The aws-ml-blog explores how the new G7e instances address these exact infrastructure challenges by bringing next-generation GPU architecture to managed cloud environments. The publication details the technical specifications and performance gains of the G7e instances, which are powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. According to the post, these instances are designed to make high-performance inference more accessible and cost-effective for enterprise users. By outfitting each GPU with 96 GB of cutting-edge GDDR7 memory, AWS has effectively doubled the memory capacity compared to the previous-generation G6e instances. This architectural decision is highly consequential: it allows a single-node G7e.2xlarge instance to host powerful open-source foundation models-such as GPT-OSS-120B, Nemotron-3-Super-120B-A12B, and Qwen3.5-35B-A3B-entirely on a single GPU. For context, deploying a 35-billion parameter model in FP16 precision requires roughly 70 GB of memory just for the weights. The 96 GB capacity ensures ample room remains for the context window and KV cache, avoiding the need for complex tensor parallelism across multiple chips for models of this size. Furthermore, the post highlights the impressive scalability of these new instances. Nodes can be provisioned in configurations of 1, 2, 4, or 8 GPUs to match specific workload demands. At the top end of the spectrum, the G7e.48xlarge instance provides up to 768 GB of total GPU memory and up to 1600 Gbps of networking throughput. This massive aggregate capacity enables the deployment of massive LLMs-up to 300 billion parameters in FP16 precision-on a single 8-GPU node. The inclusion of high-throughput networking is critical here, as it minimizes the latency overhead when model weights are sharded across multiple GPUs. With up to 2.3x the inference performance and 1,597 GB/s bandwidth per GPU (quadrupling the per-GPU memory bandwidth of older G5 instances), the G7e lineup represents a major leap forward for production AI deployment. For engineering teams and machine learning practitioners looking to optimize their generative AI infrastructure, reduce inference costs, and simplify the deployment of large-scale models, the original publication provides essential technical context and deployment parameters. [Read the full post](https://aws.amazon.com/blogs/machine-learning/accelerate-generative-ai-inference-on-amazon-sagemaker-ai-with-g7e-instances) to understand how these new instances can be leveraged within Amazon SageMaker AI. ### Key Takeaways * AWS has introduced G7e instances on Amazon SageMaker AI, powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. * The new instances offer 96 GB of GDDR7 memory per GPU, doubling the capacity of previous G6e instances. * A single G7e.2xlarge node can host models up to 35B parameters in FP16 precision, simplifying deployment and reducing costs. * The largest G7e.48xlarge instance features 8 GPUs with 768 GB total memory, capable of serving 300B parameter models. * G7e instances deliver up to 2.3x inference performance and 1,597 GB/s bandwidth per GPU, alongside 1600 Gbps of networking throughput. [Read the original post at aws-ml-blog](https://aws.amazon.com/blogs/machine-learning/accelerate-generative-ai-inference-on-amazon-sagemaker-ai-with-g7e-instances) --- ## Sources - https://aws.amazon.com/blogs/machine-learning/accelerate-generative-ai-inference-on-amazon-sagemaker-ai-with-g7e-instances