Prefill and Decode for Concurrent Requests - Optimizing LLM Performance

What changed

At TNG, we are self-hosting numerous Large Language Models on our cluster of 24 H100 GPUs. It supports 50 different applications, handles over 5,000 inferences per hour, and generates more than ten million tokens every day. Most LLMs generate text token by token, which guarantees that every new token is computed based on all preceding tokens (this model property is called auto-regressive).

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• How Long Prompts Block Other Requests - Optimizing LLM Performance
• Optimizing your LLM in production
• Efficient Request Queueing – Optimizing LLM Performance

• How Long Prompts Block Other Requests - Optimizing LLM Performance similarity 0.42
• Optimizing your LLM in production similarity 0.34
• Efficient Request Queueing – Optimizing LLM Performance similarity 0.31