model
Model served under vLLM on the remote GPU instance in this walk-through.
No evidence-backed relationships are recorded.
Getting Gemma 4 E2B to serve on a 2019-era Tesla T4 under vLLM 0.29.0 turns on a clamp inside the Triton attention kernel, and the QAT checkpoint's advantage shows up in host memory before it shows up in tokens per second.
Reality
Two arms on the same laptop differ by one flag. The small card wins because llama.cpp leaves Gemma 4's 1.93 GB per-layer embedding table in mmap and pulls a few rows per token, so only about 1.08 GB of body is resident.
Reality
An AWS EC2 rig with mcp unbounded in requirements.txt picked up mcp 2.2.0 and stopped importing. The server edit is two lines; the pip resolver and the test suite's camelCase attribute reads are where the work sits.
Reality
A dated pricing run puts the Graviton2-hosted G5g 20 percent below the Intel G4dn per hour, but that host resolves a container image with no kernels for its own T4G, so it compiles vLLM before serving a token.
Reality
An rmcp walk-through concedes its tools are I/O-bound at 100-500ms per AWS call, then makes the rewrite argument on footprint, dependency isolation and schema drift instead.
Reality