4 posts
In Post I-02, we saw that PagedAttention enables different requests to share physical KV cache blocks on the same replica. Two requests with the same system prompt can point to the same physical blocks rather than storing duplicate copies. That sharing mechanism is real and it works -- but only i...
Post I-00 established that LLM inference has two phases with fundamentally different resource profiles. Prefill processes all input tokens in parallel and is compute-bound -- the GPU's arithmetic units are the bottleneck. Decode generates tokens one at a time and is memory-bandwidth-bound -- the ...
Post I-00 traced a single request through the inference pipeline: prefill processed all input tokens in parallel, decode generated output tokens one at a time, and the KV cache grew with every step. At the end of that trace, we noted that 49 other agents were submitting queries at roughly the sam...
You have built a travel copilot. A user types a query, your application sends it to an LLM provider's API, and a few seconds later a response streams back. From the application developer's perspective, that is one function call. From the infrastructure's perspective, that function call triggers a...