Getting Started with AI Inference

 

While model training is typically a periodic event, inference runs continuously in production environments, making it the primary operational expense of enterprise AI deployments.

 

Large language models (LLMs) with tens or hundreds of billions of parameters demand immense GPU memory to store weights and intermediate states like Key-Value (KV) caches.

 

As concurrent requests and input prompt lengths increase, basic serving environments suffer from memory bottlenecks, underutilized hardware, and high latency.

 

To deploy interactive real-time applications efficiently, organizations must adopt full-stack inference performance engineering to balance throughput, response speed, and infrastructure costs without compromising output accuracy.

 

 

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