Model Overview
Description:
The NVIDIA MiniMax-M2.7 DFlash model is the DFlash draft head of MiniMax AI's MiniMax-M2.7 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check
here. The NVIDIA MiniMax-M2.7 DFlash model incorporates DFlash speculative decoding with Model Optimizer.
This model is for demonstration purposes and not for production usage.
License/Terms of Use:
Governing Terms: Use of this model is governed by the
NVIDIA Software and Model Evaluation license.
ADDITIONAL INFORMATION :
Non-Commercial MiniMax License.
Copyright (c) 2026 MiniMax
Deployment Geography:
Global
Use Case:
Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks.
Release Date:
Reference(s):
Model Architecture:
Architecture Type: Transformers
Network Architecture: MiniMax M2 (MoE)
This model was developed based on MiniMaxAI/MiniMax-M2.7.
Number of model parameters 1.31B (DFlash draft module, including the token embedding and LM head over the target vocabulary)
Input:
Input Type(s): Text
Input Format(s): String
Input Parameters: 1D (One Dimensional): Sequences
Other Properties Related to Input: Context length 196608 (trained at 4096; extended at export via YaRN rope_scaling)
Output:
Output Type(s): Text
Output Format: String
Output Parameters: 1D (One Dimensional): Sequences
Other Properties Related to Output: Maximum context length 196608 (the draft exports a YaRN rope_scaling to match the MiniMax-M2.7 target's context window)
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:
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Blackwell
- NVIDIA Hopper
Preferred Operating System(s):
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 (v1) is trained with nvidia-modelopt 0.44.0
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 DFlash modules.
Data Modality: Text
Text Training Data Size: [Less than a Billion Tokens]
Data Collection Method by dataset: [Hybrid: Automated, Synthetic]
Labeling Method by dataset: [Hybrid: Automated, Synthetic]
Properties: 469,568 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
Data Collection Method by datasetHybrid: Human, Synthetic
**Labeling Method by dataset:**Hybrid: Human, Synthetic
Properties: 3,300 multi-turn dialogue sequences, each annotated with expert preference votes.
Inference:
Acceleration Engine: vLLM
Test Hardware: NVIDIA Hopper (H100)
DFlash Speculative Decoding
Synthesized data was obtained from MiniMax AI's MiniMax-M2.7 model, which is then used to finetune the DFlash modules. This model is ready for inference with vLLM in DFlash speculative decoding mode. DFlash modules are used to predict candidate tokens beyond the next token. In the generation step, each forward DFlash module generates a distribution of tokens beyond the previous. The longest accepted candidate sequence is selected so that more than 1 token is returned in the generation step. The mean number of tokens emitted per step is the acceptance length.
Usage
To serve the checkpoint with
vLLM in DFlash speculative-decoding mode:
1vllm serve <MiniMax-M2.7 checkpoint> \
2 --speculative-config '{"method": "dflash", "model": "<DFlash checkpoint>", "num_speculative_tokens": 7}' \
3 --tensor-parallel-size 4 \
4 --max-model-len 8192 \
5 --trust-remote-code
num_speculative_tokens is the draft length (the DFlash block_size is 8, so up to 7 speculative tokens per step). Lower it to trade peak acceptance length for lower per-step draft cost.
Evaluation
We report acceptance length (AL) — the mean number of tokens emitted per decoding step (the accepted draft tokens plus the one bonus token from the target model). Higher is better; with draft length d the maximum is d+1.
Measurement setup: measured with the
specdec_bench harness on vLLM DFlash speculative decoding, greedy decoding (temperature 0), 4096-token generations, target
MiniMaxAI/MiniMax-M2.7, on NVIDIA H100. We list two draft lengths —
3 (for comparison with other published speculative-decoding cards) and
7 (DFlash
block_size − 1, the recommended serving configuration). The released draft ships with a YaRN
rope_scaling in its config so it drafts correctly at the target's full context length (see
Long-context below); the acceptance lengths reported here are for that shipped configuration.
Acceptance length on
MT-Bench (80 prompts):
| Category | AL (draft len 3) | AL (draft len 7) |
|---|
| writing | 3.04 | 3.26 |
| roleplay | 2.73 | 2.99 |
| reasoning | 2.82 | 2.63 |
| math | 3.55 | 3.78 |
| coding | 3.56 | 3.65 |
| extraction | 3.25 | 3.12 |
| stem | 2.79 | 2.84 |
| humanities | 2.67 | 2.34 |
| Overall Average | 3.05 | 3.08 |
Acceptance length on
SPEED-Bench (qualitative subset, 880 prompts):
| Category | AL (draft len 3) | AL (draft len 7) |
|---|
| coding | 3.10 | 3.31 |
| humanities | 2.63 | 2.77 |
| math | 3.09 | 3.27 |
| multilingual | 3.34 | 3.56 |
| qa | 2.81 | 3.03 |
| rag | 3.11 | 3.29 |
| reasoning | 3.02 | 3.18 |
| roleplay | 2.57 | 2.70 |
| stem | 2.73 | 2.82 |
| summarization | 2.79 | 2.94 |
| writing | 2.68 | 2.75 |
| Overall Average | 2.90 | 3.06 |
Long-context (32k)
The DFlash draft trains on a short window but must draft for the target at long context, so
the released draft exports a YaRN rope_scaling (factor 48, original_max_position_embeddings
4096) that extends its effective context to the target's full window. Acceptance length on a
32k-token throughput benchmark (draft len 7): 2.61.
Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards below.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns
here.
SUBCARDS:
Explainability
| Field: | Response: |
|---|
| Intended Task/Domain: | Text generation, reasoning, summarization, and question answering. |
| Model Type: | Text and Image-to-text transformer |
| Intended Users: | This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency. |
| Output: | Text String(s) |
| Describe how the model works: | Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers |
| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |
| Technical Limitations & Mitigation: | The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. Therefore, before deploying any applications of this model, developers should perform safety testing and tuning tailored to their specific applications of the model. |
| Verified to have met prescribed quality standards? | Yes |
| Performance Metrics: | Accuracy, Throughput, and user-side throughput |
| Potential Known Risk | The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |
| Licensing: | Your usage is governed by the following Governing Terms: Use of this model is governed by the NVIDIA Software and Model Evaluation license. |
ADDITIONAL INFORMATION :
Non-Commercial MiniMax License.
Copyright (c) 2026 MiniMax |
Bias
| Field: | Response: |
|---|
| Participation considerations from adversely impacted groups protected classes in model design and testing: | Not Applicable |
| Measures taken to mitigate against unwanted bias: | N/A |
| Bias Metric: | Not Applicable |
Safety & Security
| Field: | Response: |
|---|
| Model Application(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning |
| Describe life critical application (if present): | Not Applicable |
| Use Case Restrictions: | Abide by the Governing Terms: Use of this model is governed by the NVIDIA Software and Model Evaluation license. |
| ADDITIONAL INFORMATION : Non-Commercial MiniMax License. Copyright (c) 2026 MiniMax | |
| Model and Dataset Restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog. |
Privacy
| Field: | Response: |
|---|
| Generatable or Reverse engineerable personal data? | No |
| Personal data used to create this model? | No |
| Was consent obtained for any personal data used? | Not Applicable |
| How often is dataset reviewed? | Before Release |
| Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | No |
| Is there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Not Applicable |
| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable |
| Applicable NVIDIA Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ |
