nvidia/Llama-3.3-70B-Instruct-Eagle3
Model Overview
Description:
The NVIDIA Llama-3.3-70B Eagle model is the Eagle head of Meta's Llama-3.3-70B model, which is an auto-regressive language model that uses a dense multilayer perceptron (MLP) architecture with 70 billion parameters. For more information, please check here. The NVIDIA Llama-3.3-70B Eagle3 model incorporates Eagle speculative decoding with TensorRT Model Optimizer.
This model is ready for commercial/non-commercial use. <br>
License/Terms of Use:
Use of this model is governed by the NVIDIA Open Model License. ADDITIONAL INFORMATION: Llama 3.3 Community Model License. Built with Llama.
Deployment Geography:
Global <br>
Use Case: <br>
Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. <br>
Release Date: <br>
Hugging Face 12/16/2025 via [https://huggingface.co/nvidia/Llama-3.3-70B-Instruct-Eagle3] <br>
Model Architecture:
Architecture Type: Transformers <br> Network Architecture: Llama-3.3-70B <br> This model was developed based on [https://huggingface.co/nvidia/Llama-3.3-70B-Instruct-NVFP4] <br> Number of model parameters 3.2*10^9 <br>
Input:
Input Type(s): Text <br> Input Format(s): String <br> Input Parameters: One Dimensional (1D): Sequences <br> Other Properties Related to Input: 128k max context <br>
Output:
Output Type(s): Text <br> Output Format: String <br> Output Parameters: One-Dimensional (1D): Sequences <br> Other Properties Related to Output: 128k max output <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. <br>
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):
- v1.0-BF16: December 16th, 2026
Training and Evaluation Datasets:
The total size (in number of data points): 503.3K <br> Total number of datasets: 2<br> ** Dataset partition: Training 100%<br>
Training Dataset:
Link: ultrachat_200k and Magpie-Llama-3.1-Pro-300K-Filtered, 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. Click the links above for more information regarding the dataset. <br>
** Data Modality [Text]
** Data Collection Method by dataset <br>
- Hybrid: Synthetic, Human, Automated<br>
** Labeling Method by dataset <br>
- Hybrid: Synthetic, Human, Automated<br> Properties: 500K samples, majority synthetic, others sourced from commercially-friendly datasets. <br>
Evaluation Dataset: <br>
Link: MTBench, for more details, see here <br>
** Data Collection Method by dataset <br>
- Hybrid: Human, Synthetic<br>
** Labeling Method by dataset <br>
- Hybrid: Human, Synthetic<br>
Properties: 3,300 multi-turn dialogue sequences, each annotated with expert preference votes.<br>
Inference:
Engine: TensorRT-LLM 1.2.0rc0 <br> Test Hardware: B200 <br>
Eagle Speculative Decoding
Synthesized data was obtained from Meta's Llama-3.3-70B 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. Then, a tree-based attention mechanism samples some candidate sequences for the original model to validate. 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 <Llama-3.3-70B checkpoint> --host 0.0.0.0 --port 8000 --backend pytorch --max_batch_size 32 --tp_size 8 --extra_llm_api_options extra-llm-api-config.ymlextra-llm-api-config.yml is like this
enable_attention_dp: false
enable_autotuner: false
cuda_graph_config:
max_batch_size: 32
enable_padding: true
speculative_config:
decoding_type: Eagle
max_draft_len: 3
speculative_model_dir: <eagle3 checkpoint>
eagle3_layers_to_capture: [-1]
kv_cache_config:
enable_block_reuse: false
Note that the only layer from the target passed to the draft is the final hidden state post LayerNorm and pre-LMHead.
Evaluation
The Eagle acceptance rate benchmark results (MT-Bench) with draft length 3 are presented in the table below for medium reasoning:
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. When downloaded or used in accordance with our terms of service, 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.
For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.
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