Unveiling the Magic: Scaling Large Language Models to Serve Millions

A single short prompt can exhaust your GPU resources. Learn how a custom proxy and clever rate-limiting can serve large language models to millions of users.

#1about 3 minutes

Understanding the benefits of self-hosting large language models

Self-hosting LLMs provides greater control over data privacy, compliance, cost, and vendor lock-in compared to using third-party services.

#2about 4 minutes

Architectural overview for a scalable LLM serving platform

A scalable LLM service requires key components for model acquisition, inference, storage, billing, security, and request routing.

#3about 7 minutes

Choosing an inference engine and model storage strategy

Using network file storage (NFS) is crucial for reducing startup times and enabling fast horizontal scaling when deploying new model instances.

#4about 5 minutes

Building an efficient token-based billing system

Aggregate token usage with tools like Redis before sending data to a payment provider to manage rate limits and improve system efficiency.

#5about 3 minutes

Implementing robust rate limiting for shared LLM systems

Prevent system abuse by implementing both request-based and token-based rate limiting, using estimations for output tokens to protect shared resources.

#6about 3 minutes

Selecting the right authentication and authorization strategy

Bearer tokens offer a flexible solution for managing authentication and fine-grained authorization, such as restricting access to specific models.

#7about 2 minutes

Scaling inference with Kubernetes and smart routing

Use tools like KServe or Knative on Kubernetes for intelligent autoscaling and canary deployments based on custom metrics like queue size.

#8about 3 minutes

Summary of best practices for scalable LLM deployment

Key strategies for success include robust rate limiting, modular design, continuous benchmarking, and using canary deployments for safe production testing.