Case study
Local LLM Deployment & Serving
Self-hosted LLMs on owned GPUs serving two production pipelines, cutting cost per document from ~$0.35 to ~$0.10.
Local LLMsQWENLlamaGPU InferenceKV CacheCUDADocker
Problem
Production pipelines depended on third-party LLM APIs: ongoing per-token costs, rate limits, and sensitive medical data leaving infrastructure.
Naive open-source serving wasted GPU memory and could not keep up with production load.
Solution
We deployed serving instances across RTX 4090s and an A100 80GB, wired into extraction and chatbot pipelines.
Serving was optimized with KV cache management, prefix caching, and request batching sized to GPU memory budgets.
Outcomes
Two self-hosted instances serve production traffic.
Inference cost per document dropped from ~$0.35 to ~$0.10; sensitive documents stayed in-house.
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