Three Gemma 4 Deployments on One T4G for Under $3: What the Runtime Changes, and What It Doesn't

This article provides a step by step comparison of three Gemma 4 deployments on a single AWS hosted GPU enabled system. A suite of Python MCP tools is built to simplify management of each deployment, and one benchmark harness is shared across all three so that the runtime is the only variable. https://github.com/xbill9/gemma4-dev The whole exercise cost under three dollars, and that is the part worth keeping. Nineteen instances and about four and a half instance-hours bought three serving sweeps, nine timed boots and a handful of A/B restarts. It also bought five wrong claims, each caught by measuring instead of reasoning. On hardware where a run is expensive, the cheapest of those five would have shipped with a caveat attached. What is this project trying to Do? Three rigs in this monorepo serve google/gemma-4-E2B-it on an AWS G5g instance. One runs vLLM, one runs a pure JAX port, one runs PyTorch with transformers. The hardware is identical and only the runtime slot moves, so this should be the cleanest A/B available. For months it was not, because each rig measured itself with its own harness and quoted its own number. Three harnesses computing three statistics is not a comparison. Prerequisites An AWS account with G-family quota in us-east-1 . Each g5g.2xlarge is 8 vCPU, so 16 vCPU of spot quota runs two at once. A subnet, a security group opening TCP 8000, and an instance profile carrying AmazonSSMManagedInstanceCore plus read on the Hugging Face token secret. A Hugging Face token in Secrets Manager. It is fetched at boot into a root-only EnvironmentFile and never appears in user data. boto3 and the standard credential chain. No AWS CLI shell-outs, no inbound SSH rule, and no private key anywhere in the flow. AWS EC2 G5g Instance g5g.2xlarge — 8 vCPU, 16 GiB host Host CPU AWS Graviton2, aarch64 GPU 1x NVIDIA T4G, Turing, SM 7.5 GPU memory 15,360 MiB per nvidia-smi ; AWS lists 16,384 nominal G5g is the only family AWS ships that puts an NVIDIA GPU behind a Gravi