> 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 - How to Run Locally

Run the new GLM-5.3 model by Z.ai.

GLM-5.3 is Z.ai’s new 744B parameter (40B active) model. As of Aug 2026, GLM-5.3 is the **strongest open-model** to date, achieving SOTA on Terminal Bench 3.0 and Agents' Last Exam. GLM-5.3 uses the same base model as [GLM-5.2](/docs/models/glm-5.2.md), with every gain coming from post-training. The model has a **1M context** window and can now run locally via [Unsloth Dynamic](https://unsloth.ai/docs/basics/dynamic-3.0-ggufs) GGUFs with llama.cpp or [Unsloth Desktop](#run-glm-5.3-in-unsloth).

<a href="/docs/models/glm-5.3-flash.md" class="button primary">GLM-5.3-Flash Guide</a><a href="/pages/Vh7nCmycTp5VqtHwO5Cj#run-glm-5.3-tutorials" class="button secondary">GLM-5.3 Guide</a>

{% hint style="info" %}
If you want to run [**GLM-5.3-Flash**](/docs/models/glm-5.3-flash.md), please read our [specific article](/docs/models/glm-5.3-flash.md) for it.
{% endhint %}

Dynamic 1-bit GGUF reaches **\~76%** top-1 accuracy while being **85% smaller**. Dynamic 2-bit reaches **\~81%** accuracy while being **83% smaller**. As GLM-5.3 has same size and arch as GLM-5.2, most requirements / settings are the same. Thanks Z.ai for Unsloth day-zero access. [**GLM-5.3-GGUF**](https://huggingface.co/unsloth/GLM-5.3-GGUF)

### **⚙️ Usage Guide**

The 2-bit dynamic quant `UD-IQ2_M` uses **239GB** of disk space works well on **256GB RAM** devices like a 2x NVIDIA DGX Sparks or a Mac Studio.

The **1-bit** quant will fit on 223GB RAM and 8-bit requires 810GB RAM.

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

| 1-bit | 2-bit | 3-bit     | 4-bit     | 6-bit | 8-bit |
| ----- | ----- | --------- | --------- | ----- | ----- |
| 223GB | 245GB | 290-360GB | 372-475GB | 570GB | 810GB |

For best performance, make sure your total available memory, including VRAM and system RAM, exceeds the quantized model file size by a comfortable margin.

<figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FJsaB5Sn5JEJXD40RU06i%2Fglm53_unsloth_dynamic_ggufs_top1_accuracy_updated.png?alt=media&amp;token=ef24f54e-c3a7-461e-aa02-ccb8f654fede" alt=""><figcaption></figcaption></figure>

#### Recommended Settings

GLM-5.3 has **3 thinking modes**: **Low**, **High**, and **Max**. Use Max Thinking for complicated coding tasks. In [Unsloth Desktop](/docs/desktop.md) you can easily toggle Low, High, and Max Thinking with a UI.

Use these settings for most use cases:

| Default Settings (Most Tasks) | Long agentic tasks  |
| ----------------------------- | ------------------- |
| `temperature` = 1.0           | `temperature` = 1.0 |
| `top_p` = 0.95                | `top_p` = 1.0       |

The **maximum context window** is `1,048,576`.

GLM-5.3 uses max reasoning by default and thinking cannot be disabled. `reasoning_effort` can be `low`, `high`, or `max`.

`clear_thinking` is false by default and they recommend true.

For reasoning effort customization (change 'low' to 'high' or 'max'):

```bash
--chat-template-kwargs '{"reasoning_effort":"low"}'
```

For multi-turn chat, use `clear_thinking=true` to remove reasoning from previous turns (recommended for this model):

```bash
--chat-template-kwargs '{"reasoning_effort":"max","clear_thinking":true}'
```

### Chat Template fixes

We found GLM uses an interesting `.{id}.` notation for chat templates, but many engines don't support this. We edited it to all `[id]` so Python list indexing syntax. See this [commit change](https://huggingface.co/unsloth/GLM-5.3/commit/05cd131f7ab554f983b81c6be97916450b0ff8d2) for more details.

<figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2F3d64OwoEjqpJ5fHum860%2Fimage.png?alt=media&amp;token=784da534-c703-4246-886d-bd058cf345f1" alt="" width="563"><figcaption></figcaption></figure>

## Run GLM-5.3 Tutorials:

You can now run GLM-5.3 in [llama.cpp](https://unsloth.ai/docs/models/glm-5.3#run-glm-5.3-in-llama.cpp) and [Unsloth Desktop](#run-glm-5.3-flash-in-unsloth). We will be utilizing the 239GB [`UD-IQ2_M`](https://huggingface.co/unsloth/GLM-5.3-GGUF/tree/main/UD-IQ2_M) quant for the best balance of accessibility and accuracy.

### 🦥 Run GLM-5.3 in Unsloth

GLM-5.3 can now run in [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), MLX 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 %}

<figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FYVcpYHcS7vpnYcZUiwpO%2Fglm52%20example.png?alt=media&amp;token=d218ed4e-5102-48a6-943a-7d6b7a10446f" alt=""><figcaption></figcaption></figure>
{% 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).

<a href="https://unsloth.ai/download" class="button primary" data-icon="down-to-bracket">Download Unsloth</a>

* <i class="fa-apple">:apple:</i> [Download for macOS](https://unsloth.ai/download/mac)
* <i class="fa-windows">:windows:</i> [Download for Windows](https://unsloth.ai/download/windows)
* <i class="fa-linux">:linux:</i> [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

Go to [Unsloth Chat](/docs/new/studio/chat.md) or Model hub and search for GLM-5.3 in the search bar and download your desired model and quant.

<figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2Fpcug1oswnDUemsv08ffL%2FScreenshot%202026-08-28%20at%209.45.17%E2%80%AFAM.png?alt=media&amp;token=716b05f9-b0e9-4433-a592-6f9fdcec13a3" alt=""><figcaption></figcaption></figure>
{% endstep %}

{% step %}

#### Run GLM-5.3

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 with Unsloth API

You can use `unsloth run` command and serve GLM-5.3 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-GGUF:UD-IQ2_M
```

{% endcode %}
{% endstep %}

{% step %}

#### Unsloth is now ready

You can also do many other things with GLM-5.3 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

<figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FhxlaXPPPrWDFXhdkSdck%2FScreenshot%202026-08-27%20at%2011.59.01%E2%80%AFPM.png?alt=media&amp;token=a2ef8037-b657-473d-83e6-a5f5f22208ff" alt=""><figcaption></figcaption></figure>
{% endstep %}
{% endstepper %}

### 🦙 Run GLM-5.3 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](https://https/github.com/ggml-org/llama.cpp) for fast local inference. GGUF: [**GLM-5.3-GGUF**](https://huggingface.co/unsloth/GLM-5.3-GGUF)&#x20;

{% stepper %}
{% step %}
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.

```bash
apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/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 %}
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-5.3-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.

```bash
export LLAMA_CACHE="unsloth/GLM-5.3-GGUF"
./llama.cpp/llama-cli \
    -hf unsloth/GLM-5.3-GGUF:UD-IQ2_M \
    --temp 1.0 \
    --top-p 0.95 \
    --min-p 0.01
```

{% endstep %}

{% step %}
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.md)

```bash
hf download unsloth/GLM-5.3-GGUF \
    --local-dir unsloth/GLM-5.3-GGUF \
    --include "*UD-IQ2_M*" # Use "*UD-Q8_K_XL*" for near full precision
```

If you want to use the dynamic 1-bit, then do:

{% code overflow="wrap" expandable="true" %}

```bash
hf download unsloth/GLM-5.3-GGUF \
    --local-dir unsloth/GLM-5.3-GGUF \
    --include "*UD-IQ1_S*"
```

{% endcode %}
{% endstep %}

{% step %}
Then run the model in conversation mode. Use `unsloth/GLM-5.3-GGUF/UD-IQ2_M/GLM-5.3-UD-IQ2_M-00001-of-00006.gguf` for 2bit or `unsloth/GLM-5.3-GGUF/UD-IQ1_S/GLM-5.3-UD-IQ1_S-00001-of-00006.gguf`  for 1bit.

{% code overflow="wrap" %}

```bash
./llama.cpp/llama-cli \
    --model unsloth/GLM-5.3-GGUF/UD-IQ2_M/GLM-5.3-UD-IQ2_M-00001-of-00006.gguf \
    --temp 1.0 \
    --top-p 0.95 \
    --min-p 0.01
```

{% endcode %}
{% endstep %}

{% step %}
Similar to GLM-5.2, when you launch llama-cli, you will see:

<figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FUalNNvFxH613C9kcM3r4%2Fimage.png?alt=media&amp;token=f376e93d-26af-472f-8ea3-0968b57b004c" alt="" width="375"><figcaption></figcaption></figure>

Then after prompt, we made a cool small Snake game using `UD-IQ1_S` so 1-bit and it worked well with GLM-5.3!

<figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FDmysmr1aSotb5nIbLou5%2Fglm53-ezgif.com-video-to-gif-converter.gif?alt=media&amp;token=dff4c0a8-cf4f-43e8-99a0-40430ab8153b" alt="" width="395"><figcaption></figcaption></figure>
{% endstep %}
{% endstepper %}

#### 📐Long context via KV Cache quantization

To utilize long context in llama.cpp, use KV cache quantization to reduce memory usage.

Currently, these KV cache dtypes are supported: `f32`, `f16`, `bf16`, `q8_0`, `q4_0`, `q4_1`, `iq4_nl`, `q5_0`, and `q5_1`. By default `f16` is used. `q4_1` uses around 5 bits per weight, allowing around **3.2x longer context lengths**.

```bash
./llama.cpp/llama-cli \
    --model unsloth/GLM-5.3-GGUF/UD-IQ2_M-/GLM-5.3-UD-IQ2_M-00001-of-00006.gguf \
    --temp 1.0 \
    --top-p 0.95 \
    --min-p 0.01 \
    --cache-type-k q4_1 \
    --cache-type-v q4_1 \
    --jinja \
    --chat-template-kwargs '{"reasoning_effort":"max"}'
```

### Quantization Analysis

We ran KLD for the quants we uploaded as well and it shows Q4\_K\_XL and Q5\_K\_XL are very close to the baseline, so aim for those.

<figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2F1ClHNDXzzv9r4bLywjEJ%2Fglm53_unsloth_dynamic_ggufs_kld_benchmarks_updated.png?alt=media&amp;token=9e4677e8-df89-4718-acf2-5c14aad393c9" alt=""><figcaption></figcaption></figure>

| quant        |    GB | top-1 % | mean KLD | 99.9% KLD |    PPL |
| ------------ | ----: | ------: | -------: | --------: | -----: |
| UD-IQ1\_S    | 216.7 |   72.56 | 0.687991 |     9.104 | 4.6130 |
| UD-IQ1\_M    | 228.5 |   75.64 | 0.565455 |     8.595 | 4.1410 |
| UD-IQ2\_M    | 238.6 |   78.53 | 0.453992 |     7.717 | 3.7433 |
| UD-Q2\_K\_XL | 253.9 |   80.93 | 0.374219 |     7.076 | 3.5048 |
| UD-IQ3\_XXS  | 281.7 |   84.15 | 0.272796 |     6.252 | 3.2482 |
| UD-Q3\_K\_XL | 343.0 |   88.86 | 0.141406 |     4.127 | 2.9107 |
| UD-IQ4\_XS   | 365.3 |   90.59 | 0.101496 |     3.177 | 2.8460 |
| UD-Q4\_K\_XL | 467.3 |   94.29 | 0.036922 |     1.309 | 2.7006 |
| UD-Q5\_K\_XL | 562.5 |   95.82 | 0.019728 |     0.786 | 2.6842 |
| UD-Q6\_K\_XL | 684.4 |   96.59 | 0.013257 |     0.534 | 2.6771 |

### 📊 Benchmarks

You can view GLM-5.3's key benchmark improvements in table format further below:

<div><figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2Fm0D8JgqiMhFBvxu1IBfm%2Fglm53bench.jpg?alt=media&amp;token=fe3e5fdf-c5e8-45a2-8a7a-941fdcd5ec6b" alt=""><figcaption></figcaption></figure> <figure><img src="https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2Ftqb0wwb4uhbrmjB3bOAr%2Fglmcodingper.png?alt=media&amp;token=fc306ac0-a542-4675-b361-ccac23b691a8" alt=""><figcaption></figcaption></figure></div>

| Benchmark                               | GLM-5.3   | GLM-5.2 | Kimi K3 | <p>DeepSeek-V4</p><p>Pro-0813</p> | Qwen3.8-Max | Opus 4.8 | <p>Fable 5</p><p>(w/ fallback)</p> | GPT-5.6 Sol |
| --------------------------------------- | --------- | ------- | ------- | --------------------------------- | ----------- | -------- | ---------------------------------- | ----------- |
| Coding                                  |           |         |         |                                   |             |          |                                    |             |
| Terminal Bench 2.1                      | 88.2      | 81.0    | 88.3    | 87.9                              | 86.6        | 85.0     | 88.0                               | 88.8        |
| Terminal Bench 3.0                      | 28.3      | 4.6     | 17.4    | -                                 | -           | 21.1     | 33.7                               | 34.6        |
| <p>DeepSWE</p><p>v1.1</p>               | 66.9      | 46.2    | 67.5    | 62.7                              | 56.6        | 58.0     | 69.7                               | 72.7        |
| NL2Repo                                 | 58.0      | 48.9    | 58.0    | 61.1                              | 55.9        | 69.7     | -                                  | -           |
| <p>ProgramBench</p><p>Almost Solved</p> | 19.0      | 9.5     | 17.5    | -                                 | 10.5        | 15.5     | 33.0                               | 23.0        |
| FrontierSWE                             | 78.1      | 67.5    | -       | -                                 | -           | 66.5     | 88.2                               | -           |
| <p>SWE-Marathon</p><p>v1.1</p>          | 42.5      | 19.4    | 48.1    | -                                 | -           | 48.8     | 33.1                               | 42.5        |
| PostTrainBench                          | 39.8      | 31.7    | 32.0    | -                                 | -           | 32.9     | 41.8                               | 36.2        |
| Cyber                                   |           |         |         |                                   |             |          |                                    |             |
| CyberGym                                | 84.5      | 77.2    | 80.0    | 83.3                              | 78.5        | 78.1     | 83.8                               | 83.6        |
| <p>ExploitGym</p><p>2h / 6h</p>         | 105 / 130 | 29 / 39 | 36 / 70 | -                                 | 14 / 26     | 80 / 120 | 181 / 247                          | 216 / 293   |
| ExploitBench                            | 54.4      | 24.4    | 32.2    | -                                 | 28.8        | 40.0     | 78.0                               | 76.5        |
| Agentic                                 |           |         |         |                                   |             |          |                                    |             |
| Toolathlon Verified                     | 73.0      | 59.9    | 76.5    | 74.1                              | 72.5        | 76.2     | 74.7                               | 74.9        |
| <p>AutomationBench</p><p>v1.0.6</p>     | 48.2      | 26.2    | 46.7    | 43.2                              | 39.8        | 41.0     | 46.2                               | 45.8        |
| <p>Agents' Last Exam</p><p>ALE-CLI</p>  | 28.5      | 23.8    | 27.6    | 25.7                              | 27.0        | 25.7     | 23.8                               | 28.6        |
| HLE w/ Tools                            | 62.5      | 54.7    | 59.8    | 60.0                              | 56.2        | 57.9     | 63.9                               | 64.5        |
| GDPval-AA v2                            | 1769      | 1508    | 1682    | 1590                              | 1739        | 1588     | 1743                               | 1730        |


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://unsloth.ai/docs/models/glm-5.3.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.