Tag: LLM serving

Learn how to choose optimal batch sizes for LLM serving to cut cost per token by up to 87%. Discover real-world results, batching types, hardware trade-offs, and proven techniques to reduce AI infrastructure costs.

KV caching and continuous batching are essential for fast, affordable LLM serving. Learn how they reduce memory use, boost throughput, and enable real-world deployment on consumer hardware.

Recent-posts

Enterprise Adoption, Governance, and Risk Management for Vibe Coding

Enterprise Adoption, Governance, and Risk Management for Vibe Coding

Dec, 16 2025

E-Commerce Product Discovery with LLMs: How Semantic Matching Boosts Sales

E-Commerce Product Discovery with LLMs: How Semantic Matching Boosts Sales

Jan, 14 2026

Secure Prompting for Vibe Coding: How to Ask for Safer Code

Secure Prompting for Vibe Coding: How to Ask for Safer Code

Oct, 2 2025

Reinforcement Learning from Prompts: How Iterative Refinement Boosts LLM Accuracy

Reinforcement Learning from Prompts: How Iterative Refinement Boosts LLM Accuracy

Feb, 3 2026

Private Prompt Templates: How to Prevent Inference-Time Data Leakage in AI Systems

Private Prompt Templates: How to Prevent Inference-Time Data Leakage in AI Systems

Aug, 10 2025