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Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6

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Benchmark two 30B Mixture-of-Experts models, Qwen3-Coder-30B and NVIDIA Nemotron-3-Nano-30B, across G5, G6, G6e, and G7 GPU instances on Amazon SageMaker AI. Compare throughput, latency, and cost-per-token, and see how G7's NVIDIA Blackwell GPUs deliver measurable price-performance gains for real-time LLM inference.

Excerpt from the source

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-Experts (MoE) models across three GPU instance families on Amazon…

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Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and…