Gemma 4 on Amazon SageMaker: QAT Weights Decode 2.05x Faster Than bf16 on One L4
This article gives a short background on Amazon SageMaker real-time endpoints, then measures Gemma 4 E2B's quantization-aware trained (QAT) checkpoint against the full-size bf16 release on the same NVIDIA L4 endpoint. A suite of Python MCP tools is built to simplify management of the vLLM hosted deployment. https://github.com/xbill9/sagemaker-gemma Models google/gemma-4-E2B-it (bf16) and…
Amazon SageMaker's real-time endpoints enable quick deployment of models for efficient inference. This article compares the performance of two Gemma 4 checkpoints: the full-size bf16 release and the quantization-aware trained (QAT) checkpoint with 4-bit weights, both running on the same NVIDIA L4 endpoint. The QAT checkpoint decodes text at 105.1 tokens per second, while the bf16 checkpoint decodes at 51.3 tokens per second.
In serving mode, the QAT checkpoint handles 1077.25 tokens per second with 16 parallel requests, compared to 619.1 tokens per second for the bf16 checkpoint. The QAT checkpoint achieved this performance with only 4-bit weights, saving 18% of GPU memory compared to the bf16 checkpoint. The deployment process is straightforward, requiring a SageMaker quota of at least 1 instance type in a region with L4 capacity.
The comparison demonstrates the potential benefits of using QAT checkpoints for faster inference and memory efficiency on Amazon SageMaker.
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