Ministral 3 8B Instruct 2512

A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.

This model is the instruct post-trained version in FP8, fine-tuned for instruction tasks, making it ideal for chat and instruction based use cases.

The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 8B can even be deployed locally, capable of fitting in 12GB of VRAM in FP8, and less if further quantized.

Learn more in our blog post and paper.

Key Features

Ministral 3 8B consists of two main architectural components:

• 8.4B Language Model
• 0.4B Vision Encoder

The Ministral 3 8B 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.
• Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
• 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

Perfect for balanced performance in local or embedded systems, combining versatility with efficiency.

• Chat interfaces in constrained environments
• Local daily-driver AI assistant
• Image/document description and understanding
• Translation and content generation
• Specialized agentic use cases
• Fine-tuning and specialization
• And more...

Bringing advanced AI capabilities to resource-constrained environments.

Recommended Settings

We recommend deploying 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.

Ministral 3 Family

Other formats available here.

Benchmark Results

We compare Ministral 3 to similar sized models.

Reasoning

Instruct

Base

Usage

The model can be used with the following frameworks;

• vllm: See here
• transformers: 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

Due to their size and the FP8 format of their weights Ministral-3-3B-Instruct-2512, Ministral-3-8B-Instruct-2512 and Ministral-3-14B-Instruct-2512 can run on a single 1xH200 GPU.

A simple launch command is:

vllm serve mistralai/Ministral-3-8B-Instruct-2512 \
 --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 assume that the model mistralai/Ministral-3-8B-Instruct-2512 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.

Transformers

You can also use Ministral 3 8B Instruct 2512 with Transformers !

Transformers recently added support for FP8, so make sure to install from main:

uv pip install git+https://github.com/huggingface/transformers

To make the best use of our model with Transformers make sure to have installed mistral-common >= 1.8.6 to use our tokenizer.

pip install mistral-common --upgrade

Try it out by running the following snippet.

On latest main as of 05/12/2025, by default a FP8 triton kernel for fast accelerated matmuls (w8a8_block_fp8_matmul_triton) will be used without any degradation in accuracy. However, if you want to run your model in BF16 see (here)

Then load our tokenizer along with the model and generate:

Transformers BF16

Transformers allows you to automatically convert the checkpoint to Bfloat16. To do so, simply load the model as follows:

from transformers import Mistral3ForConditionalGeneration, FineGrainedFP8Config

model_id = "mistralai/Ministral-3-8B-Instruct-2512"
model = Mistral3ForConditionalGeneration.from_pretrained(
 model_id,
 device_map="auto",
 quantization_config=FineGrainedFP8Config(dequantize=True)
)

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.

Base model

mistralai/Ministral-3-8B-Base-2512

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A collection of edge models, with Base, Instruct and Reasoning variants, in 3 different sizes: 3B, 8B and 14B. All with vision capabilities. • 9 items • Updated Dec 2, 2025 • 170