Infrastructure · Guide
AI Infrastructure: Serving, Scaling, and Cost
Run models in production — vLLM, Ollama, gateways, quantization, GPUs, cost and latency.
Running models in production means serving them, scaling them, and controlling cost and latency. This guide covers deploying open models with vLLM, running local models with Ollama, model gateways like LiteLLM, quantization and inference optimization, GPUs and TPUs, embedding pipelines at scale, and monitoring for model drift.
Generate your own lesson →What you'll learn
- GPUs, TPUs, and AI Hardware
- Local Models with Ollama
- Model Registries and Versioning
- Cost and Latency Optimization
- Deployment Patterns for AI Apps
- Hugging Face Transformers in Production
- Model Gateways with LiteLLM
- Monitoring Model Drift and Quality
- Serving LLMs with vLLM
- Embedding Pipelines at Scale
Lessons in this guide (10)
GPUs, TPUs, and AI Hardware
Understand accelerator tradeoffs for training, inference, and cost planning.
Local Models with Ollama
Use local model serving for demos, privacy-sensitive prototypes, and dev loops.
Model Registries and Versioning
Track models, prompts, retrieval indexes, and eval sets as release artifacts.
Cost and Latency Optimization
Control token, retrieval, tool, and serving costs without gutting quality.
Deployment Patterns for AI Apps
Choose hosted APIs, self-hosted models, routers, and async jobs deliberately.
Hugging Face Transformers in Production
Use Transformers for models while knowing when to switch to serving runtimes.
Model Gateways with LiteLLM
Route across providers with fallback, budgets, and unified interfaces.
Monitoring Model Drift and Quality
Detect quality shifts from model, prompt, retrieval, and data changes.
Serving LLMs with vLLM
Run open-weight models behind an OpenAI-compatible high-throughput server.
Embedding Pipelines at Scale
Batch, version, backfill, and monitor embeddings for RAG systems.
Frequently asked questions
How do you serve an LLM in production?
Use an inference server like vLLM (for open models on your own GPUs) or a hosted API. It batches requests, manages the KV cache, and streams tokens — behind a gateway that handles routing, retries, and rate limits.
What is a model gateway?
A model gateway (e.g. LiteLLM) is a single API in front of many providers/models. It handles routing, failover, cost tracking, caching, and rate limiting so your app code doesn't change when you swap models.
How do you cut LLM cost and latency?
Stream responses for perceived speed, cache and reuse prompts, route easy calls to smaller models, quantize open models, and trim context to what's needed. Most latency is output-token generation, so shorter outputs help most.