Minecraft Player Builds Working Language Model From 445,000 Command Blocks Without Mods

An enthusiast going by the handle _objz has built a functioning generative neural network inside vanilla Minecraft that can converse with the player. The computational array consists of 445,782 command blocks and generates text directly in-game without any mods, plugins, or datapacks.

The initial prototype spanned nearly 1.91 million blocks, but the creator managed to shrink the build by more than four times by adapting the neural network’s architecture.

I made a generative AI, only using 400k command blocks (PoC)
by u/_objz in MinecraftCommands

Ternary weights architecture and block math

The model’s vocabulary includes 2,048 words, its context window considers the last six words of the conversation, and its hidden layer features 256 neurons. To output each subsequent word, the circuit performs roughly 622,000 operations.

As reported by Tom’s Hardware, using ternary weights helped bypass the game engine’s computational limits. Each parameter in the neural network takes on only three discrete values: −1, 0, or +1. This made it possible to replace heavy mathematical multiplication operations with simple addition, subtraction, or clock-cycle skip loops.

Model training and inference speed

According to the author’s post in the Reddit community, the model was trained outside the game using Python and Rust on the DailyDialog dataset (11,118 everyday conversations), after which the finished weights were translated into block coordinates and commands.

The bot can hold a simple conversational dialogue in English, but it is not meant for mathematical calculations or complex factual answers. Its perplexity score (a metric measuring model uncertainty) reached 38.8 points.

At standard engine speeds (20 ticks per second), generating a single word takes 3.2 seconds, but boosting the server to 35 ticks per second reduces latency to 1.8 seconds. Designing and assembling the entire structure took the creator about 70 hours.