# GLM-5.2 – How to Run Locally > Source: > Published: 2026-06-19 14:49:38+00:00 # GLM-5.2 - How to Run Locally Run the new GLM-5.2 model by Z.ai on local hardware! 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](/docs/basics/unsloth-dynamic-2.0-ggufs) 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 Tutorials](/docs/models/glm-5.2#run-glm-5.2-tutorials)[Quantization Results](/docs/models/glm-5.2#quantization-analysis) ** **⚙️ Usage Guide **⚙️ 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) 223 GB 245 GB 290-360 GB 372-475 GB 570 GB 810 GB For best performance, make sure your total available memory, including VRAM and system RAM, exceeds the quantized model file size by a comfortable margin. ### 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](/docs/models/glm-5.2#run-glm-5.2-in-unsloth-studio) you can easily toggle High + Max Thinking and non-Thinking with a UI. Use these settings for most use cases: `temperature` = 1.0 `temperature` = 1.0 `top_p` = 0.95 `top_p` = 1.0 **Maximum context window:**`1,048,576` . GLM 5.2 uses thinking mode by default. And supports `reasoning_effort` as "high", "max" or disabled thinking. To disable thinking, use `--chat-template-kwargs '{"enable_thinking":false}'` If you're on **Windows** Powershell, use: `--chat-template-kwargs "{\"enable_thinking\":false}"` Use 'true' and 'false' interchangeably. You can also use `--reasoning on` or `--reasoning off` in llama.cpp as well now! ### 📈 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](/docs/models/glm-5.2#run-in-llama.cpp) and [Unsloth Studio](/docs/models/glm-5.2#run-glm-5.2-in-unsloth-studio). We will be utilizing the 239GB [ UD-IQ2_M](https://huggingface.co/unsloth/GLM-5.2-GGUF/tree/main/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](/docs/new/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: Search, download, [run GGUFs](/docs/new/studio#run-models-locally)and safetensor models+**Self-healing** tool calling**web search****Code execution**(Python, Bash)[Automatic inference](/docs/new/studio#model-arena)parameter tuning (temp, top-p, etc.)Fast CPU + GPU inference via llama.cpp [Train LLMs](/docs/new/studio#no-code-training)2x faster with 70% less VRAM **Install and Launch Unsloth** To install, run in your terminal: MacOS, Linux, WSL: Windows PowerShell: **Launch Unsloth** MacOS, Linux, WSL and Windows: Then open `http://127.0.0.1:8888` (or your specific URL) in your browser. **Launch Unsloth securely with HTTPS and Cloudflare** **NEW! **Unsloth now provides a secure way to launch Studio over HTTPS through a free Cloudflare tunnel. Use the below (works in Windows, Mac & Linux): **Search and download GLM-5.2** Unsloth Studio automatically offloads to RAM and detects multiGPU setups. On first launch you will need to create a password to secure your account and sign in again later. Then go to the [Studio Chat](/docs/new/studio/chat) tab and search for **GLM-5.2** in the search bar and download your desired model and quant. Ensure you have enough compute the run the model. **Run GLM-5.2** Inference parameters should be auto-set when using Unsloth Studio, however you can still change it manually. You can also edit the context length, chat template and other settings. For more information, you can view our [Unsloth Studio inference guide](/docs/new/studio/chat). ### 🦙 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](llama.cpphttps://github.com/ggml-org/llama.cpp) for fast local inference. GGUF: **GLM-5.2-GGUF**** ** Obtain the latest `llama.cpp` **on** [ GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference. **For Apple Mac / Metal devices**, set `-DGGML_CUDA=OFF` then continue as usual - Metal support is on by default.You can now use `llama.cpp` directly to load and download models, just like `ollama run` . First, select the quantization type you want like `UD-IQ2_M` . Also use `export LLAMA_CACHE="unsloth/GLM-5.2-GGUF"` to force `llama.cpp` to save to a specific location. **Note this download process might be very slow**, so it's probably best to use the manual download process in the next section. If you want to download the model manually **(much faster!)**, we can download the model via the code below (after installing `pip install huggingface_hub` ). If downloads get stuck, see: [Hugging Face Hub, XET debugging](/docs/basics/troubleshooting-and-faqs/hugging-face-hub-xet-debugging) If you want to use the dynamic 1bit, then do: Then run the model in conversation mode. Use `unsloth/GLM-5.2-GGUF/UD-IQ2_M/GLM-5.2-UD-IQ2_M-00001-of-00006.gguf` for 2bit or `unsloth/GLM-5.2-GGUF/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00001-of-00006.gguf` for 1bit. ### 📐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](https://github.com/ggml-org/llama.cpp/pull/21038) 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: **Reasoning** HLE 40.5 49.8* 41.4* 45 31 41.4 37 37.7 HLE (w/ Tools) 54.7 57.9* 52.2* 51.4* 52.3 53.5 - 48.2 CritPt 20.9 20.9 27.1 17.7 4.6 13.4 3.7 12.9 AIME 2026 99.2 95.7 98.3 98.2 95.3 97 - 94.6 HMMT Nov. 2025 94.4 96.5 96.5 94.8 94 95 84.4 94.4 HMMT Feb. 2026 92.5 96.7 96.7 87.3 82.6 97.1 84.4 95.2 IMOAnswerBench 91.0 83.5 - 81 83.8 90 - 89.8 GPQA-Diamond 91.2 93.6 93.6 94.3 86.2 90 93 90.1 **Coding** SWE-bench Pro 62.1 69.2 58.6 54.2 58.4 60.6 59 55.4 NL2Repo 48.9 69.7 50.7 33.4 42.7 47.2 42.1 35.5 DeepSWE 46.2 58 70 10 18 18 20 8 ProgramBench 63.7 71.9 70.8 39.5 50.9 - - 47.8 Terminal Bench 2.1 (Terminus-2) 81.0 85 84 74 63.5 75 65 64 Terminal Bench 2.1 (Best Reported Harness) 82.7 78.9 83.4 70.7 69 - - - FrontierSWE (Dominance) 74.4 75.1 72.6 39.6 30.5 - - 29.0 PostTrainBench 34.3 37.2 28.4 21.6 20.1 - - - SWE-Marathon 13.0 26.0 12.0 4.0 1.0 - - - **Agentic** MCP-Atlas (Public Set) 76.8 77.8 75.3 69.2 71.8 76.4 74.2 73.6 Tool-Decathlon 48.2 59.9 55.6 48.8 40.7 - - 52.8 Last updated Was this helpful?