lightvector/KataGo v1.17.1

OpenCL, Windows, TRT Bugfixes

on GitHub

This is a quick bugfix release on top of v1.17.0 to fix three bugs, including a major crash bug for OpenCL. See v1.17.0 for the much longer list of changes new in v1.17.x!

A new release optimizing various backends further and offering faster/better alternative to OpenCL for AMD GPUs and Intel NPU and such is still planned to come before too long! (Thanks to many people for continuing to report these issues and helping to explore the options!)

KataGo is continuing to improve at https://katagotraining.org/ and if you'd like to donate your spare GPU cycles and support it, it could use your help there!

New Transformer Models (same as v1.17.0)

Included in this release for download are three very strong transformer models of various sizes that newly work with these 1.17.x releases:

• b10c384h6nbttflrs.bin.gz - smallest, stronger per visit than the strongest b18 main run models but usually as fast or faster than them
• b10c512h8nbt3tflrs-fson-silu-rsnh.bin.gz - medium, stronger per visit than the strongest b28 main run models but usually as fast or faster than them
• b11c768h12nbt3tflrs-fson-silu.bin.gz - largest transformer so far, stronger than the b40 zhizi main run models but usually as fast or faster than them

(see here for pytorch .ckpt versions of these transformers)

See https://katagotraining.org/networks/ for the main run models so far which still all work too. The main run as of the time of this release has not switched to transformers since this is the first version supporting them, but will switch soon once enough contributors upgrade to v1.17.0. See also https://katagotraining.org/extra_networks/ for other unusual models that can be used with KataGo.

Getting Started / Choosing a Backend

If you're a new user, this section has tips for getting started and basic usage! For choosing a backend:

• If you have an NVIDIA GPU, use CUDA+CUDNN or TensorRT. You'll also have to install CUDA and one of CUDNN or TensorRT from nvidia depending on your choice.

  • TensorRT - Usually best performance but longer startup times.
    • For transformers, i.e. the best models, prefer CUDA 13 + TensorRT 10.16, older versions may be outperformed by CUDA+CUDNN.
  • CUDA+CUDNN - Faster startup times than TensorRT, still pretty good performance, occasionally actually fastest.
    • For transformers, i.e. the best models, usually slightly slower than TRT 10.16 (but not always), and faster than older TRT versions.
    • Use CUDNN >= 9.8.0 if at all possible. The CUDNN 8.9.7 builds will be a LOT slower when running transformer models.
  • (CUDA 13, CUDNN 9.24, TensorRT 10.16 builds are new in this release. TensorRT < 10 support has been dropped)

• If you have a non-NVIDIA GPU, or have an NVIDIA GPU but really don't want to install CUDNN/TensorRT, OpenCL works across GPUs but will be much slower. (Future releases will likely also add faster and better support for other GPUs, stay tuned)

• For MacOS, use the Metal backend, which you can generally get by installing KataGo from homebrew, which usually updates not too long after KataGo's own release.

• If you need a pure-CPU version of KataGo, use Eigen AVX2, or if your CPU is also ancient and Eigen AVX2 doesn't work, then try Eigen.

Other notes

• +bs50 - these are just for fun, and don't support distributed training but DO support board sizes up to 50x50. They may also be slightly slower and will use much more memory, even when only playing on 19x19, so use them only when you really want to try large boards.

• Linux executables were compiled on a 22.04 Ubuntu machine using AppImage. You will still need to install e.g. correct versions of Cuda/TensorRT or have drivers for OpenCL, etc. on your own. Compiling from source is also not so hard on Linux, see the "TLDR" instructions for Linux here.

Bugs fixed in v1.17.1

• OpenCL was crashing during tuning or contribute (#1221)
• Windows versions would fail at runtime if you built them using the latest 2026 MSVC and then tried to use them with outdated DLLs that didn't correspond to the version you built them with. (#1216)
  • (technically not something you're supposed to do but if you're copying around exes and dlls from various places you can run into this)
• TensorRT if it was encountering certain GPU errors due to other configuration issues, was also slightly too picky about them and would fail even if it could fall back to other tuning that would work.