Deploying LLMs at Scale
A step-by-step guide to production-ready large language models, including vector databases and caching layers.
Large Language Models (LLMs) have revolutionized NLP tasks, but deploying them in production brings challenges around latency, cost, and scalability. This guide walks through end-to-end deployment of an LLM-powered service.
Model Selection and Quantization
Choosing the right model—GPT-3, Llama, or open-source alternatives—involves balancing capabilities and resource requirements. We'll demonstrate how to apply 8-bit and 4-bit quantization using Hugging Face Transformers and BitsAndBytes to reduce GPU memory usage.
Serving Infrastructure with FastAPI and Uvicorn
Wrap your model in a FastAPI app for low-latency inference. Configure Uvicorn with multiple workers, leverage asyncio for parallel requests, and benchmark throughput with tools like Locust.
GPU Orchestration on Kubernetes
Deploy on a GPU-enabled Kubernetes cluster. Define resource requests for nvidia.com/gpu, configure Vertical Pod Autoscaler for memory, and pre-warm pods to avoid cold starts.
Vector Database Integration
Use Pinecone or Weaviate for semantic search: store document embeddings, perform similarity searches, and combine retrieved contexts with LLM completions to enhance accuracy.
Caching and Rate Limiting
Integrate Redis for caching frequent inferences and implement rate limiting via API Gateway or FastAPI middleware to protect the model from overload.
With these strategies, your LLM service will be performant, cost-effective, and ready for real-world traffic scenarios.