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What is xav-glm52-2box-local?

xj85770/xav-glm52-2box-local — explained in plain English

Analysis updated 2026-05-18

2ShellAudience · developerComplexity · 5/5Setup · hard

In one sentence

Run a massive 753-billion-parameter AI model across two Macs with no internet. Connect them with a cable for shared memory and get usable speed.

Mindmap

mindmap
  root((repo))
    What it does
      Runs huge AI model
      Two Macs linked
      No internet needed
      18 tokens per second
    Tech stack
      Shell scripts
      llama.cpp
      Apple Mac hardware
      IQ1_S compression
    Use cases
      Run large models offline
      Generate code demos
      Stream text live
      Render demo videos
    Setup needs
      Two Macs required
      128GB RAM each
      Single cable link
      Network patches applied
    Audience
      Advanced tinkerers
      Local AI enthusiasts
      Hardware hackers

Code map

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What do people build with it?

USE CASE 1

Run a 753-billion-parameter AI model offline using two Macs linked by a cable.

USE CASE 2

Generate a complete Pac-Man game in a single file from a single AI prompt.

USE CASE 3

Stream AI text generation live with a real-time speed counter for demos.

USE CASE 4

Render AI text output into a video for proof-of-concept recordings.

What is it built with?

Shellllama.cpp

How does it compare?

xj85770/xav-glm52-2box-localaizhigitovamir-code/n8n-automation-suiteayush-sk-pathak/claude-code-starter-pack
Stars222
LanguageShellShellShell
Setup difficultyhardmoderateeasy
Complexity5/53/52/5
Audiencedeveloperpm founderdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1day+

Requires two Macs each with 128GB RAM, a physical cable link, compiled patches for llama.cpp, and a 202GB compressed model download.

No license information is provided in the explanation, so rights to use or modify this code are unknown.

So what is it?

This repository shows how to run GLM-5.2, a very large AI model with 753 billion parameters, entirely on two Apple Mac computers with no internet connection. The model is so large that a single Mac with 128GB of memory cannot hold it, resulting in a speed of under one token per second. By connecting two Macs together with a single cable into 256GB of shared memory, the full model stays in memory and runs at about 18.5 tokens per second. The repository provides patches and scripts to reproduce this setup. One patch fixes a software crash that happens when large models communicate over a network between the two machines. Another patch adds support for a specific part of the model architecture. The launcher script checks the connection between the two computers, confirms enough memory is free, runs a test, and starts the model. It cleans up memory if anything fails. The recommended configuration uses a setting called f16 for memory and reduces the number of active experts from eight to five. This increased speed by nearly nineteen percent over the starting setup without changing the model or affecting output quality. The setup served up to 128,000 tokens of context during live testing. The model itself is compressed to about 202GB using a method called IQ1_S. The repository includes demo tools used to create proof videos. One demo asks the model to write a complete Pac-Man game in a single file. The resulting file is included and works in a browser with one character fixed. Another demo streams text generation in real time with a live speed counter, and a script renders the output into a video. The project builds on existing open source work from llama.cpp, Z.ai, and Unsloth. The model weights are open.

Copy-paste prompts

Prompt 1
I have two Macs each with 128GB of RAM connected by a cable. How do I use the scripts in xj85770/xav-glm52-2box-local to run GLM-5.2 across both machines with shared memory?
Prompt 2
Help me apply the network communication patch and architecture support patch from xj85770/xav-glm52-2box-local to llama.cpp so I can run a 753B model across two networked Macs.
Prompt 3
Using the launcher script from xj85770/xav-glm52-2box-local, configure f16 memory and five active experts for GLM-5.2 to maximize token speed on my two-Mac setup.
Prompt 4
Show me how to run the Pac-Man demo from xj85770/xav-glm52-2box-local and fix the single character issue so the generated game works in my browser.
Prompt 5
Help me use the real-time streaming demo and video render script from xj85770/xav-glm52-2box-local to record a proof video of GLM-5.2 generating text.

Frequently asked questions

What is xav-glm52-2box-local?

Run a massive 753-billion-parameter AI model across two Macs with no internet. Connect them with a cable for shared memory and get usable speed.

What language is xav-glm52-2box-local written in?

Mainly Shell. The stack also includes Shell, llama.cpp.

What license does xav-glm52-2box-local use?

No license information is provided in the explanation, so rights to use or modify this code are unknown.

How hard is xav-glm52-2box-local to set up?

Setup difficulty is rated hard, with roughly 1day+ to a first successful run.

Who is xav-glm52-2box-local for?

Mainly developer.

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