Back to blog

Install KataGo in 2026: Windows, Mac & Linux Setup Guide

Published on April 6, 2026 by StoneBase Team

KataGo is one of the strongest open-source Go AIs available today. Whether you want to understand where you went wrong in a game, explore alternative moves, or study professional-level play, KataGo gives you move-by-move analysis with win rates, score estimates, and best variations.

This guide walks you through installing KataGo on Windows, macOS, or Linux, and connecting it to StoneBase for a seamless review experience.

Step 1: Download KataGo

You’ll need the KataGo engine itself. A GPU is recommended (NVIDIA, AMD, or Apple Silicon) — KataGo also runs on CPU, but a GPU makes analysis dramatically faster.

Head to the KataGo releases page and download the right version for your system.

KataGo on Windows

  • NVIDIA GPU: Download the CUDA or TensorRT version (e.g., katago-vX.X-cuda-windows.zip).
  • AMD GPU or CPU only: Download the OpenCL version.

Extract the zip to a folder you’ll remember, such as C:\KataGo.

KataGo on macOS

  • Apple Silicon (M1/M2/M3/M4): Use the Metal backend — install via Homebrew with brew install katago, or download the macOS release from the KataGo page.
  • Intel Mac: Use the OpenCL backend.

After downloading, you may need to make the binary executable: chmod +x katago.

KataGo on Linux

  • NVIDIA GPU: Download the CUDA or TensorRT version.
  • AMD GPU: Download the OpenCL version.
  • CPU only: Download the Eigen version.

Make the binary executable with chmod +x katago and place it somewhere like ~/katago.

Step 2: Download a Neural Network

KataGo needs a neural network file (the “brain” that evaluates positions):

  • Default network (recommended): Download kata1-b18c384nbt-s9996 or newer from the KataGo networks page. The 18-block network offers a great balance of speed and strength.
  • Larger networks (stronger but slower): 40-block and 60-block networks require more GPU power. Only use these if you have a modern GPU with plenty of memory.

Save the network file in the same folder as your KataGo binary.

Step 3: Set Up the Configuration File

KataGo uses a configuration file that controls its behavior. Example configs are included in the KataGo download.

Important: StoneBase uses KataGo’s analysis engine mode, not GTP mode. Make sure you use an analysis config file (e.g., analysis_example.cfg), not a GTP config (e.g., default_gtp.cfg). Using the wrong config format will prevent KataGo from starting correctly.

If you’re not sure which config to use, StoneBase can provide one. In the Engine settings page, click Download example KataGo analysis config to get a ready-to-use template.

Key settings to review:

  • numSearchThreads: Threads KataGo uses for search. Start with 1-2 for most GPUs.
  • maxVisits: Maximum positions to evaluate per move. 500-1000 is a good starting point.

For most users, the default analysis config works well out of the box.

Step 4: Install StoneBase

StoneBase is the free desktop app that drives KataGo and gives you a full review UI. Download the latest release from the GitHub releases page and pick the installer for your platform:

  • Windows: Download StoneBase-X.Y.Z.msi, double-click, follow the wizard.
  • macOS: Download StoneBase-X.Y.Z.dmg, drag StoneBase to Applications. On first launch, macOS may block the app — go to System Settings > Privacy & Security and click Open Anyway.
  • Linux: Download stonebase_X.Y.Z_amd64.deb and install with sudo dpkg -i stonebase_X.Y.Z_amd64.deb.

Step 5: Configure KataGo in StoneBase

Open StoneBase and navigate to Settings. In the KataGo section, configure:

  1. KataGo binary path: Point this to the katago executable from Step 1.
  2. Neural network path: Point this to the .bin.gz network file from Step 2.
  3. Config file path: Point this to the .cfg analysis config from Step 3. If you don’t have one yet, click Download example KataGo analysis config in the settings page to get a ready-to-use template.

StoneBase Engine settings showing KataGo binary path, config file, and engine status

Click Save and StoneBase will verify the connection. If everything is set up correctly, you’ll see a confirmation message.

You can also fine-tune analysis parameters (max visits, threads, variations) under the Analysis tab:

StoneBase Analysis settings with visit count, threads, and variation parameters

Step 6: Analyze Your First Game

With KataGo configured:

  1. Import an SGF file or select a game from your library.
  2. Click the Analyze button in the toolbar.
  3. KataGo will evaluate each move, showing:
    • Win rate: the probability of winning for each player
    • Score estimate: the projected score difference
    • Candidate moves: the AI’s top move suggestions with their evaluations
    • Principal variation: the expected sequence of play

Navigate through the game to see how the evaluation changes at each move. Large drops in win rate highlight your biggest mistakes — those are the positions worth studying most carefully.

New to AI analysis? Read our short guide on interpreting KataGo win rate and score estimates before drawing conclusions from the numbers — misreading a 5% swing as a “blunder” is a common beginner trap.

Troubleshooting

KataGo won’t start: Make sure the binary path points to the actual katago executable (not the folder). On macOS/Linux, you may need chmod +x katago.

Analysis is very slow: If you’re running on CPU, analysis will be significantly slower. Consider a smaller network (b6 or b10), or invest in a GPU.

“Failed to load neural net” error: Double-check the network file path and that the network version matches your KataGo version. Some very old networks aren’t compatible with newer KataGo releases.

Wrong score estimates: If win rates and scores seem off, check that the rules setting for your game matches the rules it was actually played under. Japanese and Chinese rules can produce different evaluations for the same position. You can configure rules per game in StoneBase’s game settings.

Frequently Asked Questions

Does KataGo work on Mac?

Yes. On Apple Silicon Macs (M1/M2/M3/M4), KataGo runs on the Metal backend and is very fast — installable with brew install katago. On Intel Macs, use the OpenCL backend.

Do I need a GPU to run KataGo?

No, KataGo runs on CPU using the Eigen backend, but analysis will be significantly slower. Any modern NVIDIA, AMD, or Apple Silicon GPU will give you a large speedup — for serious review, a GPU is strongly recommended.

How do I update KataGo?

Download the latest release from the KataGo releases page and replace the existing katago binary. Your config file and neural network usually stay compatible across releases, but always check the release notes.

Is KataGo free?

Yes, KataGo is fully open-source and free to use. StoneBase is also free to download — a Pro tier unlocks batch analysis and other advanced features, but single-game review with KataGo is free forever.

Which KataGo network should I use?

The 18-block network (kata1-b18c384nbt) is the best default for most users — strong enough for serious study and fast on mid-range GPUs. Only move to 40-block or 60-block networks if you have a high-end GPU with plenty of VRAM.

KataGo vs Leela Zero: which is better for game review?

KataGo is actively maintained, supports score estimation (not just win rate), handles different rulesets (Japanese/Chinese/Korean), and is generally considered stronger than Leela Zero today. For modern Go game review, KataGo is the recommended choice.

What’s Next?

Once KataGo is running, you can:

  • Review individual games to find your mistakes and missed opportunities
  • Batch-analyze your entire library (Pro feature) to get AI evaluations on all your games at once
  • Explore variations by placing stones and seeing how KataGo evaluates different continuations

The combination of your own reading ability and KataGo’s superhuman evaluation is the fastest way to identify weaknesses in your game and improve.