nvidia/MiniMax-M3-DSpark

Model Overview

Description:

The NVIDIA MiniMax-M3 DSpark model is the DSpark draft head of MiniMax's MiniMax-M3 model, which is a native multimodal mixture-of-experts language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA MiniMax-M3 DSpark model incorporates DSpark speculative decoding with Model Optimizer.

This model is ready for commercial/non-commercial use.

License/Terms of Use:

Governing Terms: Use of this model is governed by the NVIDIA Open Model License.

ADDITIONAL INFORMATION : MiniMax Community License. MiniMax-M3 .

Deployment Geography:

Global

Use Case:

Developers and inference providers building multimodal AI agents, coding assistants, long-context reasoning systems, and cowork-style assistants that benefit from lower-latency speculative decoding. This model is intended for DSpark-assisted serving of MiniMax-M3 for text, image, and video understanding workflows.

Release Date:

Hugging Face 07/22/2026 via https://huggingface.co/nvidia/MiniMax-M3-DSpark

References

• Nvidia Model Optimizer: https://github.com/NVIDIA/Model-Optimizer
• MiniMax-M3 base model card
• MiniMax Sparse Attention technical report
• DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation

Model Architecture:

Architecture Type: Transformers

Network Architecture: MiniMaxM3SparseForConditionalGeneration

Number of Model Parameters: 428B in total and 23B activated

Input:

Input Type(s): Text

Input Format(s): String

Input Parameters: One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D)

Other Properties Related to Input: Context length up to 1,048,576 tokens

Output:

Output Type(s): Text

Output Format: String

Output Parameters: One Dimensional(1D): Sequences

Other Properties Related to Output: Outputs may include natural-language responses, code, structured reasoning, tool-use content, and multimodal analysis depending on application-level tooling.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Supported Runtime Engine(s):

• vLLM

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Blackwell

Preferred Operating System(s):

• Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

The model is DSpark version and is trained with nvidia-modelopt v0.45.0**

Training and Evaluation Datasets:

Training Dataset:

Link: Nemotron-Post-Training-Dataset-v2, only prompts from the datasets were used for data synthesis, (the original responses from GPT were not used), which is then used to train the DSpark modules.

Data Modality: Text, Image, Video

Image Training Data Size: Undisclosed

Text Training Data Size: [1 Billion to 10 Trillion Tokens]

Video Training Data Size: Undisclosed

Data Collection Method by dataset: Hybrid: Automated, Synthetic

Labeling Method by dataset: Hybrid: Automated, Synthetic

Properties: 2M multilingual text samples featuring prompts spanning math, code, STEM, and conversational topics. Each sample includes a synthetic response generated by the target model.

Evaluation Dataset:

Link: MTBench, for more details, see here; SPEED-Bench

Data Collection Method by dataset: Hybrid: Human, Synthetic

Labeling Method by dataset:Hybrid: Human, Synthetic

Properties: MT-Bench contains 3,300 multi-turn dialogue sequences, each annotated with expert preference votes. SPEED-Bench is a unified, diverse benchmark for speculative decoding spanning coding, humanities, math, multilingual, QA, RAG, reasoning, roleplay, STEM, summarization, and writing domains.

Inference:

Acceleration Engine: vLLM

Test Hardware: NVIDIA B300

DSpark Speculative Decoding

Synthesized data was obtained from MiniMax's MiniMax-M3 model, which is then used to finetune the DSpark modules. This model is ready for inference with vLLM in DSpark speculative decoding mode, where a parallel draft backbone proposes a block of candidate tokens, a lightweight Markov head injects intra-block token dependency, and a confidence head predicts per-position acceptance probability for scheduled verification. Compared with a purely parallel drafter, DSpark is designed to improve accepted length while preserving the latency benefits of block drafting. The longest accepted candidate sequence is selected so that more than 1 token is returned in the generation step. The number of tokens generated in each step is called acceptance rate.

Usage

To serve the checkpoint with vLLM:

vllm serve MiniMaxAI/MiniMax-M3 \
--tensor-parallel-size 4 \
--block-size 128 \
--trust-remote-code \
--speculative-config '{
"method": "dspark",
"model": "",
"num_speculative_tokens":8
}'

Alternatively, with the Python LLM API:

from vllm import LLM, SamplingParams

llm = LLM(
model="MiniMaxAI/MiniMax-M3",
tensor_parallel_size=4,
block_size=128,
trust_remote_code=True,
speculative_config={
"method": "dspark",
"model": "",
"num_speculative_tokens":8
},
)

Note: --block-size 128 (the KV-cache block size) is...