> 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/minimax-m3.md).
# MiniMax M3 - How to Run Locally
MiniMax M3 is a new **\~428B (23B active)** open model for coding, agentic workflows, cowork tasks, and multimodal chat. The multimodal model has support for text, image, and video inputs, and a **1M context** **window**. The unquantized bf16 weights are \~**855GB** and the 1-bit GGUF decreases this to just **128GB (-85%)**: [**MiniMax-M3 GGUF**](https://huggingface.co/unsloth/MiniMax-M3-GGUF)
The model performs on par with Gemini 3.1 Pro - scoring 59% on SWE-Bench Pro, 66% on Terminal-Bench 2.1, 34.8% on SWE-fficiency, and 28.8% on KernelBench Hard. Thanks MiniMax for day zero access.
{% columns %}
{% column width="50%" %}
You can now run MiniMax M3 directly in [Unsloth Studio](#unsloth-studio-guide). Example of 5-bit MiniMax M3 running locally on a single M3 Ultra 512GB via Unsloth Studio:
{% hint style="info" %}
MiniMax-M3 GGUFs are currently experimental. MiniMax-M3 itself is native multimodal, but the current experimental GGUF is **text-only** and does not support MiniMax Sparse Attention.
{% endhint %}
{% endcolumn %}
{% column width="50%" %}
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#### :gear: Usage Guide
The smallest GGUF quant, `UD-IQ1_M`, uses **128GB** of disk space. Because the file size does not include KV cache, context allocation, try to have at least **133GB RAM** to run the model. It's recommended to use `UD-IQ3_XXS` which is **159GB** for best results.
The **4-bit** `UD-IQ4_XS` quant is **208GB**, while `UD-Q4_K_XL` is **265GB**. These are better suited to 256GB+ or 512GB-class systems, multi-GPU servers, or systems with CPU RAM plus GPU offload.
**Table: Inference hardware requirements** (units = total memory: RAM + VRAM, or unified memory)
1-bit
2-bit
3-bit
4-bit
5-bit
8-bit
133 GB
148 GB
164-200 GB
213-270 GB
325 GB
460-470 GB
{% hint style="success" %}
For best performance, make sure your total available memory, including VRAM and system RAM, exceeds the quantized model file size by a comfortable margin.
{% endhint %}
#### Recommended Settings
MiniMax recommends the following parameters for best performance: `temperature=1.0`, `top_p=0.95`, `top_k=40`.
{% columns %}
{% column %}
| `temperature = 1.0` |
| ------------------- |
| `top_p = 0.95` |
| `top_k = 40` |
| {% endcolumn %} |
{% column %}
* **Maximum context window:** `1,048,576`
* Default system prompt:
{% code overflow="wrap" %}
```
You are a helpful assistant. Your name is MiniMax-M3 and was built by MiniMax.
```
{% endcode %}
{% endcolumn %}
{% endcolumns %}
## Run MiniMax-M3 Tutorials:
For this tutorial, we will use the smallest current quant, `UD-IQ1_M`, because MiniMax-M3 is large. Replace `UD-IQ1_M` with `UD-IQ4_XS`, `UD-Q4_K_XL`, or another quant if your machine has enough memory. You can now run MiniMax-M3 in [Unsloth Studio](#run-in-unsloth-studio).
🦥 Unsloth Studio Guide🦙 Llama.cpp Guide
### 🦥 Unsloth Studio Guide
{% hint style="success" %}
You can now run MiniMax M3 via [Unsloth Studio](#unsloth-studio-guide) ✨. Ensure you use > [`v0.1.463-beta`](https://github.com/unslothai/unsloth/tree/v0.1.462-beta) or `2026.6.6`.
{% endhint %}
MiniMax M3 can now be run and trained in [Unsloth Studio](/docs/new/studio.md), our new open-source web UI for local AI. Unsloth Studio lets you run models locally on **MacOS**, **Windows**, Linux and:
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{% column %}
* Search, download, [run GGUFs](/docs/new/studio.md#run-models-locally) and safetensor models
* [**Self-healing** tool calling](/docs/new/studio.md#execute-code--heal-tool-calling) + **web search**
* [**Code execution**](/docs/new/studio.md#run-models-locally) (Python, Bash)
* [Automatic inference](/docs/new/studio.md#model-arena) parameter tuning (temp, top-p, etc.)
* Fast CPU + GPU inference via llama.cpp
* [Train LLMs](/docs/new/studio.md#no-code-training) 2x faster with 70% less VRAM
{% endcolumn %}
{% column %}
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{% stepper %}
{% step %}
#### Install Unsloth
Ensure you use the latest [`v0.1.463-beta`](https://github.com/unslothai/unsloth/tree/v0.1.462-beta) or `2026.6.6`. Run in your terminal:
**MacOS, Linux, WSL:**
```bash
curl -fsSL https://unsloth.ai/install.sh | sh
```
**Windows PowerShell:**
```bash
irm https://unsloth.ai/install.ps1 | iex
```
{% endstep %}
{% step %}
#### Launch Unsloth
**MacOS, Linux, WSL and Windows:**
```bash
unsloth studio -H 0.0.0.0 -p 8888
```
Then open `http://127.0.0.1:8888` (or your specific URL) in your browser.
{% endstep %}
{% step %}
#### Search and download MiniMax M3
On first launch you will need to create a password to secure your account and sign in again.
Then go to the [Unsloth Chat](/docs/new/studio/chat.md) tab and search for MiniMax M3 in the search bar and download your desired model and quant.
{% endstep %}
{% step %}
#### Run MiniMax M3
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.md).
{% endstep %}
{% endstepper %}
### 🦙 Llama.cpp Guide
{% stepper %}
{% step %}
Obtain the SPECIFIC `llama.cpp` PR on [**GitHub here**](https://github.com/ggml-org/llama.cpp/pull/24523). 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
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
git fetch origin pull/24523/head:minimax-m3
git checkout minimax-m3
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j --target llama-cli llama-server
```
{% 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 `Q2_K_XL`. Also use `export LLAMA_CACHE="folder"` 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/MiniMax-M3-GGUF"
./build/bin/llama-cli \
-hf unsloth/MiniMax-M3-GGUF:UD-IQ1_M \
--temp 1.0 \
--top-p 0.95 \
--top-k 40
```
{% hint style="info" %}
Note: MiniMax Sparse Attention is not supported yet, so inference falls back to dense attention.
{% endhint %}
{% endstep %}
{% step %}
If you want to download the model manually, 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/MiniMax-M3-GGUF \
--local-dir unsloth/MiniMax-M3-GGUF \
--include "*UD-IQ1_M*" # Use "*UD-IQ4_XS*" for 4-bit
```
{% endstep %}
{% step %}
You can edit `--threads 32` for the number of CPU threads, `--ctx-size 32768` for context length, `--n-gpu-layers 2` for GPU offloading on how many layers. Try adjusting it if your GPU goes out of memory. Also remove it if you have CPU only inference. Remember MSA is not yet supported, so keep `--ctx-size` modest - dense attention at very long contexts will use a lot of memory.
{% code overflow="wrap" %}
```bash
./build/bin/llama-cli \
--model unsloth/MiniMax-M3-GGUF/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf \
--temp 1.0 \
--top-p 0.95 \
--top-k 40
```
{% endcode %}
{% endstep %}
{% endstepper %}
## 📊 Benchmarks
---
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