Yeah, that’s a sweet hack‑the‑clock challenge. First, chunk the model into tiny, task‑specific sub‑nets—so you only fire the parts that actually matter for a given input. Then layer in aggressive pruning and 8‑bit quantization so each piece is leaner and faster. Off‑load the heavy lifting to a custom ASIC or GPU tensor cores, but keep the data flow pipelined; don’t let one block wait on another. Finally, cache the intermediate activations in high‑bandwidth memory so you avoid redundant recomputations. That combo—modular design, aggressive compression, hardware‑tailored acceleration, and smart caching—can realistically shave inference time in half, if you nail the implementation. Keep tweaking the trade‑offs; the sweet spot’s always shifting, but it’s the next frontier of AI speed.