AI / A CONCEPT NOTE
Model Serving
putting a model behind an endpoint
Overview · mechanism
pitfall · examples
01 / THE SHORT VERSION
The idea in a few sentences.
Model serving means deploying a trained model behind an HTTP/gRPC endpoint (like vLLM, Triton) that dynamically batches queries to optimize graphics card (GPU) utility.
02 / FOLLOW THE MECHANISM
How dynamic batching works
Client queries
multiple users send text prompts to the serving endpoint simultaneously.
Batch engine
vLLM groups prompts into a single matrix block (continuous batching).
GPU execution
evaluates batched matrix queries in a single hardware cycle.
Stream response
streams tokens back to users asynchronously as they complete.
04 / COMMAND NOTES
Read the command, then the result.
Inspect the flags and arguments before trying an example. Snippets can need local setup, replacement values, or resources in your own environment.
start an OpenAI-compatible serving host locally with vLLM
python -m vllm.entrypoints.openai.api_server --model facebook/opt-125m05 / CHECK YOURSELF
Could you explain Model Serving to a teammate?
Try it out loud in two sentences: what it is, and the one detail that changes the picture. If you stall, the gap is the part to reread.
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