Most people use ChatGPT every day. But have you ever wondered what actually happens after you press Enter? The answer does not magically appear all at once.
Behind the scenes, an LLM undergoes a complex inference process.
And if you are learning LLM Engineering, AI Infrastructure, GPU Infrastructure, or GenAI for DevOps, there are three concepts you should understand:
📌 Prefill — The model processes your complete input prompt.
📌 Decode — The model starts generating the response, one token at a time.
📌 KV Cache — Instead of recalculating information from all previous tokens again and again, the model remembers useful computations and reuses them.
That last part is especially important. KV Cache can make inference much faster.
But there is a trade-off: KV Cache consumes GPU memory.
And once you start serving hundreds or thousands of users, managing that GPU memory efficiently becomes a serious infrastructure problem.
This is also why inference frameworks such as vLLM and SGLang become important.
If you are coming from a DevOps, SRE, Platform Engineering, Cloud, or Forward-Deployed Engineering background and trying to move deeper into GenAI infrastructure, this is one of those foundational concepts worth understanding.
🎥 Video:
https://www.youtube.com/watch?v=05u-bgl3ETg
I’m also starting a 90-Day Intensive Program, Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers in September.
We’ll go much deeper into:
📌 Generative AI & LLM Engineering
📌 Python & DSA
📌 System Design
📌 DevOps & SRE Automation
📌 Hands-on AI Projects
📌Morning Batch: https://www.ideaweaver.ai/purchase?product_id=6827463
📌Evening Batch: https://www.ideaweaver.ai/purchase?product_id=6827464
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Image Ref: https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/
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