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Four things in an LLM stack store four different objects, and all of them get called caching.
The KV cache stores attention tensors for one request.
Prefix caching stores those same tensors on the server, keyed by a hash chain over token IDs.
Prompt caching is the provider’s billed version of that same lookup, at 0.1x the base input rate on a read against a...
The engineering layer above the model: retrieval, context management, evaluation, and the patterns that make LLM applications reliable.
In Part 13, we prefilled a corpus once and answered from the stored cache. In
There is a habit that spread quietly among people running AI agents this year. They leave the laptop lid half-open so the agent can keep working. When they close it, the run dies.
That habit belongs to agents that run on your own machine. The hosted ones solved the lid problem long ago, and over the past year they have been solving the memory problem too, in pieces.
Here is something that confuses almost everyone who starts serving language models.
You rent a top-end data center GPU. The spec sheet says it can perform close to a thousand trillion arithmetic operations per second. You load a 70 billion parameter model onto it and start generating text. You watch the utilization monitor, and it reads high. Everything looks healthy.<...