Non-specialist builds shogi AI on one RTX 3060

The author, a software engineer who normally builds business systems, openly states that machine learning is not his specialty, and his aim was to see how far an individual could take a shogi AI on a single consumer GPU. He began with AlphaZero in mind but found it infeasible at home, since zero-to-hero self-play requires thousands of TPUs and millions of games. That led him to dlshogi, whose supervised approach uses game records from strong AIs and looks like image classification plus a Monte Carlo tree search on top.

His first network, v1, reached a 50.3% move-match rate but its value loss ran 0.30 on training versus 0.59 on tests, a clear sign of overfitting. The cause was in the labels: every position in a single game carried the same final result, so with far more positions than games, the network learned to recognize the game itself rather than judge the position. Mixing each move's recorded evaluation score, converted into a win rate, into the value target, along with a larger 15-block, 192-channel net, brought the test and training losses much closer at 0.475 and 0.44.

The search side is where the biggest jump came. Reading about 1,450 positions per second at first, the author found the bottleneck was not GPU compute but the per-iteration overhead of dispatching commands, selecting moves, and writing results back. Batching positions with a virtual loss, CUDA graphs, fp16, and an overlap of GPU compute with CPU gathering pushed throughput to about 5,500-7,000 positions per second, and the same net then beat its old self 18 games to 2. The outcome appears to hinge less on the network than on how efficiently search uses it, a reading that matters most to individual builders trying to squeeze strength from modest hardware.