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What is AI Batching? Throughput vs Latency Explained
Stop treating your AI model like a personal assistant and start treating it like a factory assembly line.
This is part of the AI, Simplified series. If you missed the previous episode, catch up on Episode #25: Streaming Responses.
In the last episode, we talked about streaming — how to drip-feed tokens to a user so the app feels snappy and responsive. It’s all about the perceived speed.
But streaming doesn’t actually make the model process data faster. In fact, if you have a thousand users hitting your API at once, streaming one token at a time for each person is an engineering nightmare.
Your GPU is like a massive, high-powered industrial furnace. If you throw in one single piece of paper to burn, the furnace still has to heat up, the fans still have to spin, and the energy cost is almost the same as if you had thrown in a whole stack of documents.
Processing one request at a time is a waste of expensive silicon. To fix this, we use Batching.
Logic Behind AI Batching
When you send a prompt to an LLM, the model doesn’t just “think.” It performs massive matrix multiplications. It takes your tokens and multiplies them against billions of parameters stored in VRAM.
