vLLM and GPU serving

Overview

• vLLM is an inference-serving system designed for efficient model execution.
• Serving techniques such as batching and attention-memory management help multiple requests share expensive hardware.
• GPU optimization is matching workload, memory, scheduling, and numerical formats to quality and latency needs.

Why this matters

A simple single-request model script usually underuses hardware or fails under concurrent traffic.

An everyday example

A commercial kitchen coordinates many orders instead of preparing each one in complete isolation.

A technical example

Benchmark concurrent requests at realistic prompt/output lengths rather than measuring one short prompt on an idle GPU.

How it works

1. Load supported model
2. Admit requests within memory limits
3. Schedule/batch token computation
4. Stream outputs
5. Monitor utilization and queues.

Architecture and flow

Inference gateway → bounded request queue → GPU serving runtime → streamed results.

The insurance assistant example

A private document-extraction service reserves interactive capacity while batching overnight work separately.

When to use it

A validated self-hosting need with substantial traffic and operational capacity.

When to avoid it

A laptop demonstration or low-volume API application rarely justifies a GPU serving platform.

Interview answer

“Efficient serving needs workload-aware batching, memory limits, admission control, and latency benchmarks, not merely a GPU capable of loading the weights.”

Benefits, trade-offs, and production considerations

• Weight memory is only one component; key/value attention cache, activations, runtime overhead, and concurrency also consume memory.
• Long contexts reduce capacity.
• Test overload and recover from out-of-memory failures.
• vLLM docs.