RequantTESTNET
The evidence behind TNet

Open research.
Exact results.

Specifications, measured results and the questions that remain open. Every number comes with its scope.

specified

272 bytes

Work claim

TNet v1: nonce, row index, piece index and a 256-byte piece. This is not the size of a complete block.

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measured

11.2 ms

CPU verification

AMD Ryzen 7 8745HS, eight threads, portable Rust build with runtime AVX2 dispatch; winning-row recomputation with epoch weights already prepared.

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measured

86.7 %

Tensor share on RTX 3090

One RTX 3090 board, CUDA 12.8, cuBLAS, frozen TNet v1 parameters, median of seven attempts. This is the share of attempt time spent on int8 GEMM.

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measured

159.5 ns/ticket

RTX 3090 ticket cost

Same RTX 3090 experiment; full batch of 65,536 rows. Hardware, power limit and batch size affect performance.

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measured

88.2 %

Tensor share on CMP 50HX

CMP 50HX, Turing, CUDA 13.3, cuBLAS, frozen TNet v1 parameters. Three measured attempts after warm-up.

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specified

512 MiB

Weights per epoch

Eight 8192 by 8192 int8 weight matrices. A node can hold both current and next epoch weights; this is not its total memory requirement.

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Why exact requantization matters

Exact integer rounding between matrix layers prevents collapsing the whole computation into one linear product.

Why verify one row

Winning pieces are verified by recomputing their row, avoiding a separate succinct proof system.

Parity across implementations

Recorded GPU tickets match Rust, and Python checks the small-instance reference vectors. Turing and Ampere experiments agree byte for byte on checked tickets.

What remains open

External review, a measured LUT kernel, the advantage of dedicated int8 GEMM hardware, additional accelerator architectures and light-client design remain open.

Research boundaries

What is still open.