AWS 基准测试：G7 Blackwell 实例在小型 LLM 推理中优于 G5/G6
原标题：Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6
AI 摘要
AWS 在 SageMaker AI 上对 30B MoE 模型（Qwen3-Coder-30B 和 NVIDIA Nemotron-3-Nano-30B）进行了 G7（Blackwell）与 G5、G6、G6e 实例的推理基准测试。结果显示，G7 凭借原生 FP4 支持和更高内存带宽，在吞吐量、延迟和成本效益上优于前代实例。文章介绍了使用 SageMaker AI 的基准测试和推荐工作流来评估配置，并指出 G7 目前仅在美东（俄亥俄）和美西（俄勒冈）区域可用。
正文节选
Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6 Choosing the right GPU instance for large language model (LLM) inference is one of the most impactful decisions you make when deploying generative AI at scale. A single generation jump can slash latency, increase throughput, and reduce cost-per-token. However, the real-world magnitude of those gains depends on model architecture, quantization format, and workload shape. In this post, we benchmark two representative 30B Mixture-of-