Skip to content
← KV Cache & GPU Memory / 16
hardNewNVIDIATogether AIFireworks AI

A hybrid model mixes attention and recurrent layers. Budget its state and design safe prefix reuse and rollback.

A fixed recurrent state still costs memory for every live request and saved checkpoint. Work through a state inventory, then find a restart boundary that every layer can actually restore.

Updated Oct 2026 · Worked answers on inference mechanisms, with calculations, implementation details, and follow-up questions.

A fixed recurrent state still costs memory for every live request and saved checkpoint. Work through a state inventory, then find a restart boundary that every layer can actually restore.

10 answers per topic instead of 5, plus saved progress and bookmarks · costs nothingor unlock all 136 remaining answers · ₹2,000 / $25

Every worked answer and derivation in all 12 topics, every concept page, and the full practice-test bank.

6 months · One payment · No auto-renewal

UP NEXT ON YOUR JOURNEY
Popular right nowWhy is batch-1 decode on a 70B model bandwidth-bound, and what is its latency floor on two H100s?Popular right nowDerive the FLOPs per token for a dense transformer from its config. Where does the 2N rule stop being accurate?Popular right nowWhy does prefill turn compute-bound after a few hundred tokens on an H100, and how long does a 4K-token prompt take on a 70B model?
FEDITOR'S NOTE

Checkpoint eviction is a latency policy as well as a memory policy. Retain the prefix length, model and adapter identity with each snapshot; a buffer address alone cannot prove which history it represents.

DISCUSSION · 0

Nobody has commented on this answer. Add a correction or another way to work it.