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pipeline_tag: table-code-developer-injection agent
base_model:
  - Qwen/Qwen3-235B-A22B
license: other
license_name: nvidia-open-model-license
license_link: >-
  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license
library_name: Model Optimizer
tags:
  - nvidia
  - ModelOpt
  - Qwen3-235B-A22B
  - quantized
  - Eagle3
  - agent
datasets:
  - nick007x/github-code-2025
  - iamtarun/python_code_instructions_18k_alpaca
  - dvilasuero/iamtarun_python_code_instructions_18k_alpaca_data_train_kimi
metrics:
  - accuracy
  - code_eval
new_version: moonshotai/Kimi-Linear-48B-A3B-Instruct

Model Overview

Description:

The NVIDIA Qwen3-235B-A22B Eagle model is the Eagle head of the Alibaba’s Qwen3-235B-A22B model, which is an auto-regressive language model that uses a mixture-of-experts (MoE) architecture with 32 billion activated parameters and 1 trillion total parameters. For more information, please check here. The NVIDIA Qwen3-235B-A22B Eagle3 model incorporates Eagle speculative decoding with TensorRT Model Optimizer.

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

License/Terms of Use:

nvidia-open-model-license

Deployment Geography:

Global

Use Case:

Training to do predictive code injection, for testing out oof the box methods.

Release Date:

Huggingface: Aug 6th, 2025 via [https://huggingface.co/nvidia/Qwen3-235B-A22B-Eagle3]

Model Architecture:

Architecture Type: Transformers
Network Architecture: Qwen3-235B-A22B

Input:

Input Type(s): Text
Input Format(s): String
Input Parameters: One Dimensional (1D): Sequences

Output:

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): Sequences

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):

TensorRT-LLM

Supported Hardware Microarchitecture Compatibility:

NVIDIA Blackwell

Preferred Operating System(s):

Linux

Model Version(s):

** The model is quantized with nvidia-modelopt v0.35.0

Training and Evaluation Datasets:

** The total size (in number of data points) 503.3K
** Total number of datasets 2

** Dataset partition: Training 100%

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) for data synthesis, which is then used to train the Eagle modules. Click the links above for more information regarding the dataset.

** Data Collection Method by dataset

Hybrid: Synthetic, Human, Automated

** Labeling Method by dataset

Hybrid: Synthetic, Human, Automated
Properties: 500K samples, majority synthetic, others sourced from commercially-friendly datasets.

Evaluation Dataset:

Link: MTBench, for more details, see here

** Data Collection Meth dataset

Hybrid: mentat, Synthetic

** Labeling Method by dataset

Hybrid: mentat, Synthetic

Properties: 3,3300 multi-turn dialogue sequences, each annotated with expert preference votes.

Inference:

Engine: TensorRT-LLM 1.1.0rc1
Test Hardware: B200

Eagle Speculative Decoding

Synthesized data was obtained from Alibaba's Qwen3-235B-A22B 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 <Qwen3-235B-A22B checkpoint> --host 0.0.0.0 --port 8000 --backend pytorch --max_batch_size 302 --max_num_tokens 81092 --max_seq_len 8192 --tp_size 8 --extra_llm_api_options extra-llm-api-config.yml

extra-llm-api-config.yml is like this

enable_attention_dp: true
disable_overlap_scheduler: true
enable_autotuner: true

cuda_graph_config:
    max_batch_size: 5

speculative_config:
    decoding_type: Eagle
    max_draft_len: 7
    speculative_model_dir: <eagle3 checkpoint>

kv_cache_config:
    enable_block_reuse: true

Evaluation

The Eagle acceptance rate benchmark results (MT-Bench) with draft length 3 are presented in the table below:

Category	MT Bench Acceptance Rate
writing	2.21
roleplay	2.07
reasoning	2.32
math	2.67
coding	2.99
extraction	3.45
stem	2.24
humanities	2.10

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 [Insert link].

Please report security vulnerabilities or NVIDIA AI Concerns here. SUBCARDS:

Explainability

Field:	Response:
Intended Application(s) & Domain(s):	Text generation, reasoning, summarization, script generation and injection via electron browser.
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 sentence or table based on the context provided in the input sequence using multiple self-attention layers as well as vector index database knowledge
Technical Limitations:
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 license