GLM-5.2 is Z.ai’s new open model, delivering SOTA performance across long-horizon coding, reasoning, and agentic tasks. With 744B parameters, 40B active parameters, and a 1M context window, it can now be run locally using Unsloth Dynamic GGUFs. GLM-5.2 is the strongest open model to date, performing on par with Claude 4.8 Opus, GPT-5.5, and Gemini 3.1 Pro across Artificial Analysis and many other benchmarks.

The full model requires 1.51TB of disk space, while Unsloth Dynamic 2-bit GGUF reduces this to 239GB (-84% size) by upcasting important layers to 8 or 16-bit. Dynamic 1-bit lowers further to 217GB (-86%). Thanks Z.ai for giving Unsloth day-zero access. GLM-5.2-GGUF

Run GLM-5.2 TutorialsQuantization Results

⚙️ Usage Guide

The 2-bit dynamic quant UD-IQ2_M uses 239GB of disk space - this can directly fit on a 256GB unified memory Mac and works well in a 1x24GB GPU and 256GB of RAM with MoE offloading. The 1-bit quant will fit on a 223GB RAM and 8-bit requires 810GB RAM.

Table: Inference hardware requirements (units = total memory: RAM + VRAM, or unified memory)

Recommended Settings

GLM-5.2 has 3 thinking modes. Non-thinking and Thinking in two modes: High + Max. Use Max Thinking for complicated tasks. In Unsloth Studio you can easily toggle High + Max Thinking and non-Thinking with a UI.

Use these settings for most use cases:

• Maximum context window: 1,048,576.

📈 Quantization analysis

We also ran KLD (KL Divergence) to gauge the accuracy of our quantizations of GLM-5.2-GGUF. In general, dynamic 4-bit UD-Q4_K_XL and dynamic 5-bit UD-Q5_K_XL are generally lossless, and smaller quants also work great!

On pure top-1% accuracy, dynamic 1-bit gets around 76.2% accuracy yet being 86% smaller! Dynamic 2-bit gets around 82% accuracy whilst being 84% smaller.

99.9% KLD is also generally good - there is a larger uplift from 4bit onwards though, so for massive out of distribution tasks, dynamic 4-bit is probably best.

The mean KLD generally follows a clear monotonic trend vs disk space, and shows even at 1-bit GLM 5.2 works well!

Run GLM-5.2 Tutorials:

You can now run GLM-5.2 in llama.cpp and Unsloth Studio. We will be utilizing the 239GB UD-IQ2_M quant for best results in terms of accessbility and accuracy.

🦥 Run GLM-5.2 in Unsloth Studio

GLM-5.2 can run in Unsloth Studio, an open-source web UI for local AI. Unsloth Studio automatically offloads to RAM and detects multiGPU setups. With Unsloth Studio, you can run models locally on MacOS, Windows, Linux and:

🦙 Run GLM-5.2 in llama.cpp

For this guide we'll be running the UD-IQ2_M quant which will require at least 245GB RAM. Feel free to change quantization type. For these tutorials, we will using llama.cpp for fast local inference. GGUF: GLM-5.2-GGUF

📐Long context via KV Cache quantization

To utilize long context in llama.cpp, we need to employ KV cache quantization to reduce memory usage. Recently llama.cpp added higher accuracy tricks to KV cache quantization - see and other PRs!

Currently, these KV cache dtypes are supported:

By default f16 is used. If you use q4_0 which is around 4.5 bits per weight, you can extend around 16 / 4.5 = 3.5x longer context lengths! So if you model used to support 10K, 35K can be in reach! q4_1 is probably better since you also get a shifting parameter, and is 5 bits per weight - so 3.2x longer contexts.

Use it like below:

📊 Benchmarks

You can view further below for GLM-5.2 benchmarks in table format: