LLM infrastructure · 2025
High-Throughput LLM Serving
An inference backend serving a LLaMA-3 8B chat model with vLLM behind a FastAPI microservice, tuned for throughput on a single 24 GB GPU.
3.1×
token throughput
The goal was a production-shaped LLM backend that a small team could run on one consumer-class GPU: fast, observable, and compatible with the tools people already use.
Serving
A LLaMA-3 8B chat model runs on vLLM behind a FastAPI microservice with JWT-based auth and request logging. Continuous batching and paged attention increased throughput 3.1× in tokens per second while keeping p95 latency under 450 ms on a single 24 GB GPU.
Policy and interface
Configurable system prompts, rate limiting and per-tenant max-token policies sit in front of the model. The REST interface is compatible with OpenAI client libraries, so existing code can point at it without changes.
Observability
Prometheus and Grafana dashboards track GPU utilisation, token throughput, error rates and request-latency histograms, making it possible to monitor and debug production-like workloads.
Next project
Affective Multimodal Assistant →
<400 ms
response latency