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.
- 170.5 tok/s single-stream, spec decode, through the serve surface — c=1, N=5 median · RTX PRO 6000 Blackwell 96GB (rented pod)
- 0.182 s / 0.003 s TTFT cold / warm — cold forced with a fresh cache_salt per request · RTX PRO 6000 Blackwell 96GB (rented pod)
- same tokens, alone or batched greedy output byte-identical at c=1 vs c=16 — same server, 16/16 on four models
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/memraResearch — 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 →- 2026-08-05
- 2026-08-05
- 2026-08-04
- 2026-08-04
- Serving is a determinism problem 16% of (layer,token) pairs
- The compiler ate my byte 1 byte store, dropped at -O3
- Dogfooding found three bugs in my server 3 bugs, 1 class