Colab

In the first One Layer Down run, I loaded TinyLlama with vLLM on a Colab T4 and generated 100 tokens. That was useful because it proved the basic path: GPU, CUDA, PyTorch, vLLM, model weights, tokenizer, and one successful generation. But it was not the latency measurement I actually needed. When an application calls a model, it can wait for the full answer and show it all at once, or it can stream partial output as the model generates....

I have been reading plenty about inference systems: KV cache, PagedAttention, prefill, decode, batching, and disaggregated serving. But reading about the machinery is different from running it and watching the machine do work. This post is the first in the series that closes that gap. The goal was intentionally small: run one open-source model on a GPU, record what environment I actually got, generate 100 tokens, and capture enough numbers to make the next experiment less hand-wavy....