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AWS published a benchmark comparing 30B Mixture-of-Experts models (Qwen3-Coder and Nemotron) on G7 vs G5 and G6 instances, showing G7 delivers better performance and lower cost-per-token, a critical factor for regional enterprises adopting generative AI.

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AWS G7 vs G5 and G6: A New Benchmark for 30B Model Inference Efficiency on SageMaker AI

Benchmark Overview

FAQ

What are 30B Mixture-of-Experts (MoE) models?

They are large language models with billions of parameters (e.g., 30 billion) but only activate a small fraction per request, making them faster and cheaper to run than traditional models of the same size.

How do AWS G7 instances compare to G5 and G6?

According to AWS's benchmark, G7, based on NVIDIA Blackwell architecture, offers higher throughput (requests per second), lower latency, and lower cost-per-token compared to G5 (A10G) and G6 (L4) when running 30B MoE models.

Should MENA enterprises adopt G7 now?

Yes, especially for organizations running real-time, cost-sensitive applications. The benchmark shows a clear improvement in performance-per-dollar, accelerating ROI for generative AI projects.

Which practical use cases benefit most from G7?

Applications requiring immediate responses like chatbots, coding assistants, recommendation systems, and real-time document analysis benefit most. These are common in the region's banking, government, and energy sectors.

Source: AWS Machine Learning

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