Instructions to use ggml-org/Mistral-Large-4-1T-A52B-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-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-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ggml-org/Mistral-Large-4-1T-A52B-GGUF:Q4_K_M

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-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ggml-org/Mistral-Large-4-1T-A52B-GGUF:Q4_K_M

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-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf ggml-org/Mistral-Large-4-1T-A52B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use ggml-org/Mistral-Large-4-1T-A52B-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-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-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-GGUF:Q4_K_M

Ollama
How to use ggml-org/Mistral-Large-4-1T-A52B-GGUF with Ollama:
```
ollama run hf.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ggml-org/Mistral-Large-4-1T-A52B-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-GGUF:Q4_K_M

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-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ggml-org/Mistral-Large-4-1T-A52B-GGUF with Docker Model Runner:
```
docker model run hf.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ggml-org/Mistral-Large-4-1T-A52B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Large-4-1T-A52B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ggml-org/Mistral-Large-4-1T-A52B-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-GGUF:Q4_K_M

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-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ggml-org/Mistral-Large-4-1T-A52B-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-GGUF:Q4_K_M

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-GGUF:Q4_K_M" \
  --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

Run with https://llama.app

llama serve -hf ggml-org/Mistral-Large-4-1T-A52B-GGUF:IQ2_XXS

Source models

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

Related models

ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF: GGUF of the NVFP4 release, with the original NVFP4 experts and the DSpark draft head
mistralai/Mistral-Large-4-1T-A52B-NVFP4: Mistral's NVFP4 release

Files

Quant	Size	Notes
BF16	2.10 TB	Lossless upcast of the FP8 release
Q8_0	1.12 TB	Near-lossless
Q4_K_M	635 GB	4-bit quant
Q2_K	381 GB	2-bit quant. Fits in 512 GB of unified memory
IQ2_XXS	298 GB	imatrix-calibrated. Fits in 512 GB of unified memory
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 source checkpoint is block-scaled FP8. All GGUFs here are quantized from it; for a version that keeps the original NVFP4 experts, see ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF.
The IQ2_XXS output quantizes the routed expert gate/up projections to IQ2_XXS and the expert down projections to IQ2_XS. Attention, the sparse-attention indexer, the shared experts, the dense first layer, the embeddings and the output head are kept at Q8_0.
The IQ2_XXS is calibrated with an imatrix computed on 36 chunks of 8,192 tokens.
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.

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GGUF

Model size

1T params

Architecture

mistral-large4

Hardware compatibility

2-bit

4-bit

8-bit

16-bit

View +2 variants

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

Base model

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

Quantized

(18)

this model