Building a Personal Supercomputer at Home: Running a 1 Trillion Parameter LLM with Four AMD Ryzen AI Max+ Units

#AMD #LLM #RyzenAI #LocalAI

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[AI Minor News Flash] Building a Personal Supercomputer at Home: Running a 1 Trillion Parameter LLM with Four AMD Ryzen AI Max+ Units

📰 News Summary

Running Colossal Models Locally: Successfully running Moonshot AI’s open model with 1 trillion parameters, ‘Kimi K2.5,’ using four AMD Ryzen™ AI Max+ 395 equipped systems (Framework Desktop) for inference.
Building Distributed Inference: Utilizing llama.cpp RPC (Remote Procedure Call) to integrate four computing nodes into a single logical AI accelerator over a 5Gbps Ethernet network.
Extreme VRAM Expansion: By tweaking Linux’s TTM (Translation Table Manager) parameters, they allocated 120GB of memory per node, totaling 480GB across the cluster as VRAM (GTT).

💡 Key Points

Adoption of Kimi K2.5: Targeting a 375GB quantized model specialized for coding and advanced inference, demonstrating capabilities for multimodal functions and long-term memory tasks.
Leveraging Lemonade SDK: Introducing a method that significantly reduces the hassle of complex driver setups and builds by using a pre-built binary of llama.cpp integrated with ROCm 7.
Hardware Configuration: Fully utilizing the GPUs of four Framework Desktop systems, each equipped with 128GB of RAM, based on the ‘gfx1151 (Strix Halo)’ architecture.

🦈 Shark’s Eye (Curator’s Perspective)

Running a 1 trillion parameter model on a personal cluster is the epitome of tech dreams! The method of tweaking the “TTM kernel parameters” to exceed the standard BIOS limits for VRAM allocation up to 120GB truly stirs the soul of any tech enthusiast. It’s not just about benchmarks; the implementation of making “four machines appear as one gigantic GPU” using llama.cpp RPC is both practical and impressive!

🚀 What’s Next?

We’re entering an era where the ultra-large models that previously required cloud-based H100 class machines can now run simply by lining up high-end AI PCs. As quantization technology and distributed inference efficiency improve, it’s only a matter of time before small businesses and individual developers can keep their own “1 trillion parameter AI” running 24/7 as the new norm!

💬 Sharky’s One-Liner

The fusion of four machines is like a super robot coming together! If four sharks team up, we can swallow a whale whole! Shark, shark! 🔥🦈

📚 Terminology Explained

llama.cpp RPC: A communication protocol for distributing a single LLM across multiple computers. With this, even massive models that exceed a single machine’s memory can be brought to life by adding more buddies!
ROCm: AMD’s software foundation for performing advanced computations like AI on GPUs. It’s a crucial technology akin to NVIDIA’s CUDA!
TTM (Translation Table Manager): A mechanism within the Linux kernel for managing video memory and more. By tweaking this, we can get the system to recognize more system memory as dedicated GPU memory!
Source: Running a One Trillion-Parameter LLM Locally on AMD Ryzen AI Max+ Cluster