Benchmarking 30B LLMs Across SageMaker GPUs
π‘See whether G7 Blackwell GPUs lower the cost of real-time 30B LLM inference.
β‘ 30-Second TL;DR
What Changed
Evaluates two 30B Mixture-of-Experts models: Qwen3-Coder-30B and NVIDIA Nemotron-3-Nano-30B.
Why It Matters
The results can guide teams choosing infrastructure for production LLM inference. G7 may improve economics for latency-sensitive applications, but workload-specific benchmarking remains important.
What To Do Next
Run the two 30B models on your representative prompts across G6e and G7, then compare cost per token alongside latency before selecting an instance.
Key Points
- β’Evaluates two 30B Mixture-of-Experts models: Qwen3-Coder-30B and NVIDIA Nemotron-3-Nano-30B.
- β’Compares inference performance across G5, G6, G6e, and G7 GPU instances.
- β’Reports throughput, latency, and cost-per-token for real-time LLM workloads.
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Original source: AWS Machine Learning Blog β
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