Instructions to use ggml-org/Mistral-Large-4-1T-A52B-NVFP4-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 ggml-org/Mistral-Large-4-1T-A52B-NVFP4-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 ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama cli -hf ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

Llama new
LM Studio
Jan

vLLM

How to use ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ggml-org/Mistral-Large-4-1T-A52B-NVFP4-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": "ggml-org/Mistral-Large-4-1T-A52B-NVFP4-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/ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

Ollama
How to use ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4
```
Unsloth Desktop

How to use ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

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": "ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Mistral-Large-4-1T-A52B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use ggml-org/Mistral-Large-4-1T-A52B-NVFP4-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 ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

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 ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

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 "ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4" \
  --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"

Mistral Large 4 (NVFP4)

Run with https://llama.app

llama serve -hf ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF:NVFP4

Source models

https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B-NVFP4

Related models

ggml-org/Mistral-Large-4-1T-A52B-GGUF: GGUFs of the FP8 release, from BF16 down to IQ2_XXS
mistralai/Mistral-Large-4-1T-A52B: Mistral's FP8 release

Files

File	Size	Notes
NVFP4	619 GB	Routed experts in the original NVFP4, everything else in BF16
dspark (BF16)	4.2 GB	DSpark speculative decoding draft head
mmproj (BF16, F16)	3.4 / 3.4 GB	Vision encoder

Notes

Mistral Large 4 is a 1T-parameter mixture-of-experts model (128 routed experts, 4 active per token, plus 1 shared expert; ~52B active parameters) with image input and a 1M-token context window.
The routed experts are copied from Mistral's NVFP4 release without re-quantization. The FP8 layers of the release (attention, indexer query projection, shared experts and the dense first layer) are stored losslessly in BF16, as are the layers that were already BF16.
The DSpark draft head is 3 MLA layers that propose 3 tokens at a time from the target model's hidden states, with a 2,048-token sliding window.
For K-quants (Q8_0 down to IQ2_XXS) made from the FP8 release, see ggml-org/Mistral-Large-4-1T-A52B-GGUF.
Reasoning depth is set with reasoning_effort: low, medium or high (the default). Reasoning cannot be turned off.
Recommended sampling (stored in the GGUF metadata): temperature 1.0, top_p 0.95.

Downloads last month: 2,487

GGUF

Model size

1T params

Architecture

mistral-large4

Hardware compatibility

4-bit

16-bit

Model tree for ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF

Base model

mistralai/Mistral-Large-4-1T-A52B

Quantized

mistralai/Mistral-Large-4-1T-A52B-NVFP4

Quantized

(1)

this model