nvidia/Kimi-K2.6-Eagle3
Model Overview
Description:
The NVIDIA Kimi-K2.6 Eagle model is the Eagle head of Moonshot AI's Kimi-K2.6 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Kimi-K2.6 Eagle3 model incorporates Eagle speculative decoding with Model Optimizer.
With draft length 3, this Eagle head achieves an average acceptance length of 2.62 tokens per step on MT-Bench and 2.67 on the SPEED-Bench qualitative subset.
This model is ready for commercial/non-commercial use. <br>
License/Terms of Use:
Governing Terms: Use of this model is governed by the NVIDIA Open Model License.
ADDITIONAL INFORMATION : Modified MIT License. Kimi-K2.6 .
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 where latency-optimized inference via speculative decoding is desirable.
Release Date:
Hugging Face 06/03/2026 via https://huggingface.co/nvidia/Kimi-K2.6-Eagle3
Reference(s):
- Kimi-K2.6 release notes
- EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test
Model Architecture:
Architecture Type: Transformers <br> Network Architecture: DeepSeek V3 <br> Number of model parameters 1.8*10^9 <br>
Input:
Input Type(s): Text, Image, Video <br> Input Format(s): String, Binary(Base64 encoded), Binary(Base64 encoded) <br> Input Parameters: One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D) <br> Other Properties Related to Input: Context length: 256k<br>
Output:
Output Type(s): Text <br> Output Format: String <br> Output Parameters: One Dimensional(1D): Sequences <br> Other Properties Related to Output: Outputs may include natural-language responses, code, structured JSON, tool-call requests, agent coordination instructions, and generated artifacts depending on serving configuration and application-level tooling. <br>
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): <br>
- TensorRT-LLM <br>
Supported Hardware Microarchitecture Compatibility: <br>
- NVIDIA Blackwell <br>
Preferred Operating System(s): <br>
- Linux <br>
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 version 1 of Eagle3 and is trained with nvidia-modelopt v0.44.0 <br>
Training and Evaluation Datasets:
Total size (in number of data points) 112K. <br> Dataset partition: Training 100% <br>
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 Eagle modules. Data Modality: Text, Image, Video <br> Image Training Data Size: None Text Training Data Size: Less than a Billion Tokens Video Training Data Size: None Data Collection Method by dataset: Hybrid: Automated, Synthetic<br> Labeling Method by dataset: Hybrid: Automated, Synthetic<br> Properties: 112K 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 dataset: Hybrid: Human, Synthetic<br> Labeling Method by dataset: Hybrid: Human, Synthetic Properties: 3,300 multi-turn dialogue sequences, each annotated with expert preference votes.
Link: SPEED-Bench (qualitative subset) Data Collection Method by dataset: Hybrid: Human, Synthetic<br> Labeling Method by dataset: Hybrid: Human, Synthetic Properties: 880 single- and multi-turn evaluation prompts spanning 11 categories (coding, humanities, math, multilingual, qa, rag, reasoning, roleplay, stem, summarization, writing).
Inference:
Acceleration Engine: TensorRT-LLM <br> Test Hardware: NVIDIA B200 <br>
Eagle Speculative Decoding
Synthesized data was obtained from Moonshot AI's Kimi-K2.6 model, which is then used to finetune the Eagle modules. This model is ready for inference with TensorRT-LLM in Eagle speculative decoding mode. Eagle modules are used to predict candidate tokens beyond the next token. In the generation step, each forward Eagle 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 number of tokens generated in each step is called acceptance rate.
Usage
To serve the checkpoint with TensorRT-LLM, follow the sample commands below with the TensorRT-LLM GitHub repo:
trtllm-serve <Kimi-K2.6-NVFP4 checkpoint> --host 0.0.0.0 --port 8000 --backend pytorch --max_batch_size 32 --max_num_tokens 8192 --max_seq_len 8192 --tp_size 4 --extra_llm_api_options extra-llm-api-config.yml
with extra-llm-api-config.yml being
speculative_config:
decoding_type: Eagle
max_draft_len: 3
speculative_model_dir: <eagle3 checkpoint>Evaluation
Acceptance rate on MT-bench with draft length 3:
Acceptance rate on SPEED-Bench (qualitative subset) with draft length 3:
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++ Explainability, Bias, Safety & Security, and Privacy Subcards below.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>
SUBCARDS:
Explainability
ADDITIONAL INFORMATION : Modified MIT License. Kimi-K2.6 . |
Bias
Safety & Security
ADDITIONAL INFORMATION : Modified MIT License. Kimi-K2.6 . | |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.|