Gemma 4 on a Tesla T4: QAT Weights Decode 1.79x Faster Than bf16
This article provides a step by step deployment guide for **Gemma 4 E2B * to a Tesla T4 hosted GPU enabled system. A suite of Python MCP tools is built to simplify management of the vLLM hosted deployment. The T4 is already attached to the Compute Engine VM the tools run on, so there is nothing to provision: the work is getting a Turing GPU to run Gemma 4, and then finding out which checkpoint it…
Deploying Gemma 4 E2B on a Tesla T4 GPU involves a series of steps using Python MCP tools. The Tesla T4, a Turing-based GPU with 15,360 MiB of memory, is already attached to a Compute Engine VM with 2 vCPU and 7.8 GB of RAM. The deployment process begins with checking the host's capacity, verifying the GPU architecture, applying a Turing-specific patch, and ensuring vLLM can run on the GPU.
The solution involves installing a specific version of PyTorch and vLLM that are compatible with the Tesla T4's CUDA architecture. It also includes setting up a 16 GB swapfile on the large disk to accommodate the memory requirements of Gemma 4's weight loading process. With these adjustments, Gemma 4 can be successfully deployed on the Tesla T4, decoding text at a rate of 72.31 tokens per second per stream compared to 40.44 tokens per second on the bf16 model.
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