So… I have it working now. The core of an inference engine in Rust is about 3k lines (scheduling, kv/swa/recurrent state, prefix cache, multimodal, dynamic batching) I then asked Codex to implement (with MTP, cudagraphs, TP on Blackwell): - DeepSeekV4 Flash - Gemma 3.8 27B - GPT-OSS - Inkling It turns out that most models take a lot of code to run with fast, persistent graphable kernels and so on… on the order of 2.5k for a simple model (gpt-oss) and up to 7k for a monster like DeepSeekV4 Flash that has mHC, CSA, HCA and DSpark Not sure if I’ll continue with this, but it was fun! The simple core is reusable, and startup times are crazy fast for an inference engine with no tech debt and not having to serve a ton of dynamic configurations - just one model forward pass in nvfp4 :’) - maybe one day SGLang/vLLM will be modular like this. See interactivity-throughput below for Inkling-Small TP=1 after a few sconv kernel fusions. (This is without overlap scheduling btw! Don’t even really need it when your core + scheduler is small and Rust.)