> For the complete documentation index, see [llms.txt](https://unsloth.ai/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://unsloth.ai/docs/models/glm-5.3.md).
# GLM-5.3-Flash
GLM-5.3-Flash, also known as **`ox-alpha`**, is Z.ai’s new 320B parameter multimodal open model with 18B active parameters. GLM-5.3-Flash **outperforms GLM-5.2** across benchmarks and real-world workloads at one-tenth the price, while approaching **Claude Opus 4.8** on coding and agentic benchmarks.
{% hint style="info" %}
This article is a WIP. We are working on supporting the model so you can run it locally with [Unsloth Dynamic](/docs/basics/dynamic-3.0-ggufs.md) GGUFs. Thank you Z.ai for day zero access.
{% endhint %}
GLM-5.3-Flash is built on a newly trained base model redesigned for greater capability and efficiency. Its hybrid sparse and linear attention architecture lowers long-context serving costs without sacrificing accuracy, while Manifold-Constrained Hyper-Connections improve scaling. Combined with a 30T-token multimodal pre-training corpus, it delivers more intelligence with less compute.
### Recommended Settings
GLM-5.3-Flash has **3 thinking modes**: Low, High, and Max. Use Max Thinking for complicated tasks. In [Unsloth](#run-glm-5.2-in-unsloth-studio), you can easily select Low, High, or Max Thinking with a toggle in the chat area.
Use these settings for most use cases:
| Default Settings (Most Tasks) | DeepSWE |
| ----------------------------- | -------------------- |
| `temperature` = 1.0 | `temperature` = 0.95 |
| `top_p` = 0.95 | `top_p` = 1.0 |
* **Maximum context window:** `1,048,576`.
#### Changing reasoning effort
GLM-5.3-Flash uses Max reasoning by default. It also supports reasoning efforts where `reasoning_effort` can be "low", "high", or "max".
## Run GLM-5.3-Flash (Ox-Alpha) Locally
You can soon run GLM-5.3-Flash (Ox-Alpha) in Unsloth Desktop and llama.cpp with our specific PR. Feel free to change quantization type.
* Hugging Face: [GLM-5.3-Flash-GGUF](https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF)
{% hint style="warning" %}
WORK IN PROGRESS!
{% endhint %}
Run in Unsloth DesktopRun in llama.cpp
### 🦥 Run GLM-5.3-Flash in Unsloth
GLM-5.3-Flash will soon be able to run [Unsloth Desktop](#run-qwen3.8-in-unsloth-desktop), an open-source UI app for local AI. **Unsloth automatically offloads to RAM and detects multiGPU setups**. With Unsloth Desktop, you can run models locally on **MacOS, Windows**, Linux and:
{% columns %}
{% column %}
* Search, download, [run GGUFs](/docs/new/studio.md#run-models-locally) and safetensor models
* [**Self-healing** tool calling](/docs/new/studio/chat.md#auto-healing-tool-calling) + **web search**
* [**Code execution**](/docs/desktop.md#code-execution) (Python, Bash)
* [Automatic inference](https://unsloth.ai/docs/desktop#feature-deep-dive) parameter tuning (temp, top-p, etc.)
* Fast CPU + GPU inference via MLX and llama.cpp
* [Train LLMs](/docs/new/studio.md#no-code-training) 2x faster with 70% less VRAM
{% endcolumn %}
{% column %}
{% endcolumn %}
{% endcolumns %}
{% stepper %}
{% step %}
#### Install Unsloth
The easiest way to get started is by downloading the [Unsloth Desktop app](/docs/desktop.md). Works on [macOS](/docs/get-started/install/mac.md), [Windows](/docs/get-started/install/windows-installation.md), and [Linux](/docs/get-started/install/linux.md).
Download Unsloth
* :apple: [Download for macOS](https://unsloth.ai/download/mac)
* :windows: [Download for Windows](https://unsloth.ai/download/windows)
* :linux: [Download for Linux](https://unsloth.ai/download/linux)
Or, if you prefer to install manually:
MacOS, Linux, WSL:
```bash
curl -fsSL https://unsloth.ai/install.sh | sh
```
Windows PowerShell:
```bash
irm https://unsloth.ai/install.ps1 | iex
```
{% endstep %}
{% step %}
#### Search and download GLM-5.3-Flash
Go to [Unsloth Chat](/docs/new/studio/chat.md) or Model hub and search for GLM-5.3-Flash in the search bar and download your desired model and quant.
{% endstep %}
{% step %}
#### Run GLM-5.3-Flash
Inference parameters should be auto-set when using Unsloth, 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 inference guide](/docs/new/studio/chat.md).
{% endstep %}
{% step %}
#### Serve GLM-5.3-Flash with Unsloth API
You can use `unsloth run` command and serve GLM-5.3-Flash via an API using `llama-server` runtime flags, including context sizing, GPU layers, threading, sampling, networking, and tool configuration. For more info see our [API docs](/docs/basics/api.md) or [unsloth start](/docs/integrations/unsloth-start.md).
{% code overflow="wrap" %}
```bash
unsloth run --model unsloth/GLM-5.3-Flash-GGUF:UD-IQ1_S
```
{% endcode %}
{% endstep %}
{% step %}
#### Unsloth is now ready
You can also do many other things with GLM-5.3-Flash via Unsloth Desktop like:
* **Connect tools:** [Claude Code](/docs/basics/claude-code.md), [Codex](/docs/basics/codex.md), [web search](/docs/new/studio/chat.md#advanced-web-search), [MCP](/docs/basics/mcp.md) and more
* **Train models:** Fine-tune text, diffusion, [embedding](/docs/basics/embedding-finetuning.md), and more
* **Generate media:** Create and train [images](/docs/basics/diffusion-image.md), video, [TTS](/docs/basics/text-to-speech-tts-fine-tuning.md) locally
{% endstep %}
{% endstepper %}
### :llama: Run GLM-5.3-Flash in llama.cpp
{% stepper %}
{% step %}
We need to use our specific llama.cpp PR [here](https://github.com/unslothai/llama.cpp/pull/61). 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.
```bash
apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone --branch iq1-narrow https://github.com/unslothai/llama.cpp
cmake llama.cpp -B llama.cpp/build \
-DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp
```
{% endstep %}
{% step %}
To run the model, you can do:
{% code overflow="wrap" %}
```bash
pip install -U "huggingface_hub[cli]"
hf download unsloth/GLM-5.3-Flash-GGUF \
--local-dir unsloth/GLM-5.3-Flash-GGUF \
--include "*IQ1_S*" # Use "*IQ2_XXS*" for 2-bit
```
{% endcode %}
{% endstep %}
{% step %}
Then to run it:
{% code overflow="wrap" %}
```bash
./llama.cpp/llama-cli \
-hf unsloth/GLM-5.3-Flash-GGUF:IQ1_S \
--jinja \
--temp 1.0 \
--top-p 0.95 \
--chat-template-kwargs '{"reasoning_effort":"max"}' \
--fit on
```
{% endcode %}
Replace IQ1\_S with your preferred quant, such as IQ2\_XXS for 2-bit.
{% endstep %}
{% endstepper %}
## 📊 Benchmarks
| 78.0 | - | 64.3 | 75.0 | 68.0 | 65.0 |
| BabyVision | 53.4 | - | 35.1 | 46.8 | 61.6 | 70.9 |
| MVbench | 77.8 | - | 69.4 | 67.1 | 75.0 | 82.2 |
| MMVU | 80.5 | - | 72.7 | 67.4 | 75.8 | 82.3 |
---
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