tiyuvta — conclusive refutation by evidence

tiyuvta is an AI lab where the evidence decides.

tiyuvta studies model efficiency end to end — draft-head training, expert pruning and healing, precision assignment, speculative decoding — and measures every claim in memra, an inference engine built from scratch to expose what a number includes. The result can be yes, no, or not yet; the failed arms stay visible.

fig. 01 — the apparatus is part of the argument

memra — the engine

A from-scratch Rust+CUDA inference engine, public and MIT-licensed: MTP speculative decoding, FP8 checkpoint serving, MoE spill, exactness gates on Blackwell. Every kernel bit-audited against a CPU reference; every published number's raw run logs committed in-repo.

github.com/avifenesh/memra

Research — trained, not only measured

Compact MTP draft heads with their own vocabularies, pruned-and-healed MoEs, precision allocation under byte ceilings, training-recipe studies. Preregistered kill gates; refuted hypotheses published with the same care as supported ones.

the evidence ledger →

Inference — the product

The lab serves the fastest deterministic inference its engine can produce, as an OpenAI-compatible API. Serving pays for the hardware the research runs on. One model class, commitments you can verify from your own client.

the landing page →

fig. 02 — recent entries in the ledger

fig. 03 — writing