GPT-5.4 has 1M token context. Here's the engineering problem behind it.

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Everyone's talking about what GPT-5.4 can do with a million token context window. Nobody's explaining what had to be solved to get there. The bottleneck isn't the model - it's the KV cache. And at a million tokens, the memory engineering becomes the whole problem. In this video: what KV cache eviction actually is, why sliding window gets it wrong, and how labs evaluate whether long context is actually working. If you're interviewing at an AI lab, this is the kind of question that separates engineers who understand systems from engineers who just use them. * Can you explain this out loud under interview pressure? Practice on Upskill - AI mock interviewer built for ML/AI engineers. → https://tryupskill.app #machinelearning #llm #openai #mlinterview #aiengineering #kvcache #interviewprep

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