Descriptions:
Fahd Mirza demonstrates how to run GLM-5.1 — the newly open-sourced flagship agentic model from Zhipu AI’s GLM team — locally on a single NVIDIA H100 GPU combined with system RAM, using llama.cpp for inference. The video is a practical guide for anyone looking to deploy a frontier-scale mixture-of-experts model without relying on API access.
GLM-5.1 is a 744 billion parameter model with only 40 billion parameters active on any given forward pass, spread across 78 hidden layers with 256 experts (8 selected per token) and a dynamic sparse attention mechanism. It supports a 200,000 token context window. At full BF16 precision, the model requires approximately 1.65 terabytes of storage — far beyond any single consumer or professional GPU. Mirza’s solution is Unsloth’s UDIQ2MM format, a dynamic 2-bit quantization that identifies the most critical layers and upcasts them to 8-bit or 16-bit to preserve accuracy, compressing the model to 236GB while maintaining quality significantly closer to full precision than a naive 2-bit approach.
The deployment strategy uses an H100’s 80GB VRAM for as many transformer layers as possible, spills the remainder into 125GB of system RAM, and uses a 64GB swap partition as a safety buffer — totaling approximately 268GB of usable headroom. After walking through every installation command in detail, Mirza serves the model via llama.cpp and runs a live coding test. GLM-5.1 had previously been available only through Zhipu AI’s API; this video covers its first open-source release.
📺 Source: Fahd Mirza · Published April 08, 2026
🏷️ Format: Tutorial Demo
