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Gilgamesh83/
Mistral-Large-3-675B-Instruct-2512-NVFP4

Name: Mistral-Large-3-675B-Instruct-2512-NVFP4
Availability: InStock

vllm

mistral-common

compressed-tensors

mistralai/Mistral-Large-3-675B-Base-2512

finetune

apache-2.0

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Mistral Large 3 675B Instruct 2512 NVFP4

From our family of large models, Mistral Large 3 is a state-of-the-art general-purpose Multimodal granular Mixture-of-Experts model with 41B active parameters and 675B total parameters trained from the ground up with 3000 H200s.

This model is the instruct post-trained version, fine-tuned for instruction tasks, making it ideal for chat, agentic and instruction based use cases.
Designed for reliability and long-context comprehension - It is engineered for production-grade assistants, retrieval-augmented systems, scientific workloads, and complex enterprise workflows.

[!Tip] This checkpoint in particular is a post-training-activation quantized version of Mistral-Large-3-675B-Instruct-2512. It was created using llm-compressor as part of a collaboration with teams from vLLM & Red Hat. Special thanks goes out to Dipika Sikka, Kyle Sayers, Eldar Kurtić, and Tyler Michael Smith.

Mistral Large 3 is deployable on-premises in:

FP8 on a single node of B200s or H200s.
NVFP4 on a single node of H100s or A100s.

Key Features

Mistral Large 3 consists of two main architectural components:

A Granular MoE Language Model with 673B params and 39B active
A 2.5B Vision Encoder

The Mistral Large 3 Instruct model offers the following capabilities:

Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
System Prompt: Maintains strong adherence and support for system prompts.
Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
Frontier: Delivers best-in-class performance.
Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
Large Context Window: Supports a 256k context window.

Use Cases

With powerful long-context performance, stable and consistent cross-domain behavior, Mistral Large 3 is perfect for:

Long Document Understanding
Powerful Daily-Driver AI Assistants
State-of-the-Art Agentic and Tool-Use Capabilities
Enterprise Knowledge Work
General Coding Assistant

And enterprise-grade use cases requiring frontier capabilities.

Recommended Settings

We recommend deploying Large 3 in a client-server configuration with the following best practices:

System Prompt: Define a clear environment and use case, including guidance on how to effectively leverage tools in agentic systems.
Sampling Parameters: Use a temperature below 0.1 for daily-driver and production environments ; Higher temperatures may be explored for creative use cases - developers are encouraged to experiment with alternative settings.
Tools: Keep the set of tools well-defined and limit their number to the minimum required for the use case - Avoiding overloading the model with an excessive number of tools.
Vision: When deploying with vision capabilities, we recommend maintaining an aspect ratio close to 1:1 (width-to-height) for images. Avoiding the use of overly thin or wide images - crop them as needed to ensure optimal performance.

Known Issues / Limitations

Not a dedicated reasoning model: Dedicated reasoning models can outperform Mistral Large 3 in strict reasoning use cases.
Behind vision-first models in multimodal tasks: Mistral Large 3 can lag behind models optimized for vision tasks and use cases.
Complex deployment: Due to its large size and architecture, the model can be challenging to deploy efficiently with constrained resources or at scale.

Benchmark Results

[!Warning] For tasks requiring context lengths less than 32k tokens, you should see no performance degradation as compared to Mistral-Large-3-675B-Instruct-2512, for tasks requiring more context you might experience a small degradation in performance.

We compare Mistral Large 3 to similar sized models.

Usage

The model can be used with the following frameworks;

vllm: See here

vLLM

We recommend using this model with vLLM.

Installation

Make sure to install vllm >= 0.12.0:

pip install vllm --upgrade

Doing so should automatically install mistral_common >= 1.8.6.

To check:

python -c "import mistral_common; print(mistral_common.__version__)"

You can also make use of a ready-to-go docker image or on the docker hub.

Serve

We recommend to use this format if you plan to deploy Mistral Large 3 has it achieves performance similar to FP8 for less memory. However please note that for large context (> 64k) we observed a subsequent drop of performance. In such cases, please use the FP8 weights. Otherwise on B200 (Blackwell 200) we observe a significant speed-up and a minor regression on vision datasets probably due to the calibration that was performed mainly on text data.

To take full advantage of the NVFP4 format, consider using B200 GPUs that has a dedicated architecture to maximize its performance. For those who do not have access to this GPU generation, vLLM has the awesome feature to fall back to Marlin FP4 which allows you to still run the quantized model in older generations (A100, H100). You won't notice a speed-up in comparison with FP8 quantization but still benefit a memory gain.

A simple launch command is:

bash

vllm serve mistralai/Mistral-Large-3-675B-Instruct-2512-NVFP4 \
  --max-model-len 262144 --tensor-parallel-size 8 \
  --tokenizer_mode mistral --config_format mistral --load_format mistral \
  --enable-auto-tool-choice --tool-call-parser mistral

Key parameter notes:

enable-auto-tool-choice: Required when enabling tool usage.
tool-call-parser mistral: Required when enabling tool usage.

Additional flags:

You can set --max-model-len to preserve memory. By default it is set to 262144 which is quite large but not necessary for most scenarios.
You can set --max-num-batched-tokens to balance throughput and latency, higher means higher throughput but higher latency.

Usage of the model

Here we asumme that the model mistralai/Mistral-Large-3-675B-Instruct-2512-NVFP4 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.

Vision Reasoning

Let's see if Mistral Large 3 knows when to pick a fight !

python

from datetime import datetime, timedelta

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 262144

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    today = datetime.today().strftime("%Y-%m-%d")
    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
    model_name = repo_id.split("/")[-1]
    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)


SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]


response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)

print(response.choices[0].message.content)

Function Calling

Let's solve some equations thanks to our simple Python calculator tool.

python

import json
from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 262144

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt


SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")

image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"


def my_calculator(expression: str) -> str:
    return str(eval(expression))


tools = [
    {
        "type": "function",
        "function": {
            "name": "my_calculator",
            "description": "A calculator that can evaluate a mathematical equation and compute its results.",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "The mathematical expression to evaluate.",
                    },
                },
                "required": ["expression"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "rewrite",
            "description": "Rewrite a given text for improved clarity",
            "parameters": {
                "type": "object",
                "properties": {
                    "text": {
                        "type": "string",
                        "description": "The input text to rewrite",
                    }
                },
            },
        },
    },
]

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Thanks to your calculator, compute the results for the equations that involve numbers displayed in the image.",
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": image_url,
                },
            },
        ],
    },
]

response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
    tools=tools,
    tool_choice="auto",
)

tool_calls = response.choices[0].message.tool_calls

results = []
for tool_call in tool_calls:
    function_name = tool_call.function.name
    function_args = tool_call.function.arguments
    if function_name == "my_calculator":
        result = my_calculator(**json.loads(function_args))
        results.append(result)

messages.append({"role": "assistant", "tool_calls": tool_calls})
for tool_call, result in zip(tool_calls, results):
    messages.append(
        {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "name": tool_call.function.name,
            "content": result,
        }
    )


response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)

print(response.choices[0].message.content)

Text-Only Request

Mistral Large 3 can follow your instructions down to the letter.

python

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 262144

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt


SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.",
    },
]

response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)

assistant_message = response.choices[0].message.content
print(assistant_message)

License

This model is licensed under the Apache 2.0 License.

You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.

Gilgamesh83/Mistral-Large-3-675B-Instruct-2512-NVFP4

Mistral Large 3 675B Instruct 2512 NVFP4

Key Features

Use Cases

Recommended Settings

Known Issues / Limitations

Benchmark Results

Usage

vLLM

Installation

Serve

Usage of the model

License

Gilgamesh83/
Mistral-Large-3-675B-Instruct-2512-NVFP4