Serving a small, open-source LLM behind vLLM, containerized on a single desktop GPU.

The repo is here: https://github.com/codecalligrapher/llm-serving-vllm-k8s

The target is to have a single-node Kubernetes, with Grafana to monitor and a throughput benchmark.

Status: containerized OpenAI-compatible endpoint running on an RTX 3060 Ti (8GB). K8s, monitoring, and benchmark are scoped below but not yet built.

Why vLLM

Mainly because of the OpenAI-compatible server, so the endpoint is v1/chat/completions and any OpenAI client works against it.

The primary goal of langugae models is next-word/next-sequence prediction. The transformer architecture is built on the premise of “attention”, this was developed to solve the precursor’s weakness in modeling long-length text sequences (namely recursive neural networks).

In its most basic format, an RNN is a reduce over a sequence with a carried accumulator (in machine-learning terms, the current state).

For context, reduce(function, iterable, initializer) goes through the iterable from left to right, applying the function at each call. It takes the output of function(accumulator, item) at each call, returning the new accumulator and moves to the next item. The initializer is the starting accumulator/state