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Work claim
TNet v1: nonce, row index, piece index and a 256-byte piece. This is not the size of a complete block.
Read the sourceSpecifications, measured results and the questions that remain open. Every number comes with its scope.
TNet v1: nonce, row index, piece index and a 256-byte piece. This is not the size of a complete block.
Read the sourceAMD Ryzen 7 8745HS, eight threads, portable Rust build with runtime AVX2 dispatch; winning-row recomputation with epoch weights already prepared.
Read the sourceOne 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.
Read the sourceSame RTX 3090 experiment; full batch of 65,536 rows. Hardware, power limit and batch size affect performance.
Read the sourceCMP 50HX, Turing, CUDA 13.3, cuBLAS, frozen TNet v1 parameters. Three measured attempts after warm-up.
Read the sourceEight 8192 by 8192 int8 weight matrices. A node can hold both current and next epoch weights; this is not its total memory requirement.
Read the sourceExact integer rounding between matrix layers prevents collapsing the whole computation into one linear product.
Winning pieces are verified by recomputing their row, avoiding a separate succinct proof system.
Recorded GPU tickets match Rust, and Python checks the small-instance reference vectors. Turing and Ampere experiments agree byte for byte on checked tickets.
External review, a measured LUT kernel, the advantage of dedicated int8 GEMM hardware, additional accelerator architectures and light-client design remain open.