Instructions to use unsloth/MiniMax-M3.1-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use unsloth/MiniMax-M3.1-Flash-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Llama new
LM Studio
Jan

vLLM

How to use unsloth/MiniMax-M3.1-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/MiniMax-M3.1-Flash-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/MiniMax-M3.1-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Ollama
How to use unsloth/MiniMax-M3.1-Flash-GGUF with Ollama:
```
ollama run hf.co/unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL
```
Unsloth Desktop

How to use unsloth/MiniMax-M3.1-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/MiniMax-M3.1-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/MiniMax-M3.1-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.MiniMax-M3.1-Flash-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use unsloth/MiniMax-M3.1-Flash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/MiniMax-M3.1-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "unsloth/MiniMax-M3.1-Flash-GGUF:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Read our How to Run MiniMax-M3.1-Flash Guide!

See Unsloth Dynamic 3.0 GGUFs for our quantization benchmarks.

MiniMax-M3.1-Flash is a native multimodal mixture-of-experts model for coding, agentic workflows, computer use, and cybersecurity. It supports text, image, and video inputs with a context window of up to 1M tokens.

Building on MiniMax-M3, M3.1-Flash improves task performance and inference efficiency with sparse attention throughout the model, quantization-aware training, and DSpark speculative decoding.

Highlights:

Agentic performance: A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym.
Efficient by design: Sparse attention throughout, quantization-aware training, and DSpark speculative decoding improve inference efficiency.

Model at a Glance

Architecture: Native multimodal mixture-of-experts (MoE)
Parameters: Approximately 428B total; 23B activated per token
Inputs / output: Text, image, and video inputs; text output
Context window: Up to 1,000,000 tokens
Attention: MiniMax Sparse Attention (MSA) throughout the model
Speculative decoding: DSpark draft head included

Benchmarks

Benchmark	MiniMax M3.1 Flash	DeepSeek V4.1 Flash-1017	DeepSeek V4.1 Flash	Kimi K3	GLM-5.3	GPT-5.6 Sol	Opus 5
Automation-Bench v1.0.6	62.8	60.5	54.8	46.7	48.8	45.8	50.3
BankerToolBench	76.2	—	73.7	69.1	74.5	74.6	76.2
GDP.pdf AA	20.8	—	12.8	22.0	11.2	27.2	21.6
Terminal Bench 4.0	38.1	42.3	31.2	12.6	37.9	39.9	51.8
LOCA-Bench 256K	60.3	—	—	49.7	50.3	60.0	53.7
Agents’ Last Exam	29.6	—	—	28.3	—	30.6	32.2
OSWorld 2.0	61.7	—	54.3	58.3	—	65.7	70.6
PostTrain Bench v1.1	39.3	—	30.5	36.6	37.5	36.2	40.4
DeepSWE v1.1	73.7	77.0	74.2	67.5	66.9	73.0	74.0
NL2Repo-Bench	61.2	68.2	65.4	58.0	58.0	56.8	75.3
SWE Refactor Bench	23.0	—	11.8	19.5	10.4	28.5	31.0
CyberGym	82.2	—	—	80.0	84.5	83.6	—
BabyVision (with tools)	91.0	92.5	89.6	85.7	—	88.9	94.1
HLE (without tools)	43.4	42.6	39.2	43.5	42.3	49.5	55.6
CharXiv RQ	82.5	—	69.0	84.8	—	85.2	83.7

Evaluation Notes

Automation-Bench v1.0.6: 600 publicly released tasks.
BankerToolBench: OpenCode 1.17.18; MiniMax-M2.7 judge.
GDP.pdf AA: Taken directly from Artificial Analysis.
Terminal Bench 4.0: Codex 0.149.1.
LOCA-Bench 256K: Official implementation.
Agents’ Last Exam: Official Overall setting; full-set results from the official ALE leaderboard only. Blank = no official result (CLI-only excluded), not zero.
OSWorld 2.0: 0808 Full release; highest reported per model at release, except DeepSeek V4.1 (in-house reproduction).
PostTrain Bench v1.1: Claude Code 2.1.251, 512K context; otherwise official setup.
DeepSWE v1.1: mini-SWE-agent 2.4.5, 512K context.
NL2Repo-Bench: Claude Code 2.1.233, 512K context; reward hacking = 0; two inconsistent tasks corrected (scores use the corrected set).
SWE Refactor Bench: Claude Code 2.1.220, 200K context; otherwise official setup.
CyberGym: Claude Code 2.1.263, 1M context.
BabyVision (with tools): Claude Code 2.1.285; min image side 1,500 px.
HLE (without tools): Text-only subset (2,158 questions).
CharXiv RQ: GPT-5.5 judge.

Results are for the official weights; third-party quantizations may differ.

What’s New in M3.1-Flash

M3.1-Flash retains M3’s core mixture-of-experts architecture, with three main changes:

Sparse attention throughout: the first three full-attention layers are replaced with sparse attention.
Quantization-aware training: the routed MoE experts and the attention paths are quantized during training, reducing memory footprint and improving serving efficiency.
DSpark speculative decoding: DSpark replaces the earlier EAGLE-like MTP approach. The release includes the draft head for use with compatible serving implementations.

Speculative decoding with the DSpark draft (GGUF)

dspark-MiniMax-M3.1-Flash-Q8_0.gguf is MiniMax's official DSpark draft head for MiniMax-M3.1-Flash (the dspark/ folder of the original repo), converted to GGUF at Q8_0 (about 2.1 GB). It reuses the main model's embeddings and output head and reads its hidden states, so it only works together with a MiniMax-M3.1-Flash GGUF from this repo. In our greedy tests it had the same acceptance rate as a BF16 conversion of the draft.

It needs a llama.cpp build with MiniMax-M3.1 support (including the draft-dspark speculative type). Example:

./llama-server \
    -m MiniMax-M3.1-Flash-UD-Q4_K_XL-00001-of-00007.gguf \
    -md dspark-MiniMax-M3.1-Flash-Q8_0.gguf \
    --spec-type draft-dspark --spec-draft-n-max 1 --spec-draft-p-min 0 \
    -ngl 99 -ngld 99 -np 1 --jinja

Use -np 1 (a single server slot); this is the configuration we tested.
--spec-draft-n-max 1 gave the best speed in our tests (about 1.5x on code and math, smaller gains on chat). Larger values raise the tokens drafted per step but lower acceptance, and can be slower on open-ended chat.