nvidia/Qwen3.6-35B-A3B-NVFP4 · Hugging Face

Source: https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#model-overviewModel Overview

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#descriptionDescription:

The NVIDIA Qwen3.6-35B-A3B-NVFP4 model is the quantized version of Alibaba’s Qwen3.6-35B-A3B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please checkhere. The NVIDIA Qwen3.6-35B-A3B-NVFP4 model is quantized withModel Optimizer.

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

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#third-party-community-considerationThird-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA(Qwen3.6-35B-A3B) Model Cardfrom Alibaba.

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#referencesReferences

NVIDIA Model Optimizer:https://github.com/NVIDIA/Model-Optimizer

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#licenseterms-of-useLicense/Terms of Use:

Apache license 2.0

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#deployment-geographyDeployment Geography:

Global

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#use-case-Use Case:

Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#release-date–Release Date:

Hugging Face on 05/28/2026 viahttps://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#model-architectureModel Architecture:

**Architecture Type:**Transformers **Network Architecture:**Mixture-of-Experts (MoE) with Hybrid Attention **Number of Model Parameters:**35B in total and 3B activated

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#inputInput:

**Input Type(s):**Text, Image, Video **Input Format(s):**String, Red, Green, Blue (RGB), Video (MP4/WebM) **Input Parameters:**One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D) **Other Properties Related to Input:**Context length up to 262K

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#outputOutput:

**Output Type(s):**Text **Output Format:**String **Output Parameters:**One-Dimensional(1D): Sequences **Other Properties Related to Output:**None

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.

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#software-integrationSoftware Integration:

Supported Runtime Engine(s):

• vLLM

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Hopper, 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.

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#model-versionsModel Version(s):

The model version is NVFP4 1.0 version and is Quantized with nvidia-modelopt v0.44.0

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#training-and-evaluation-datasetsTraining and Evaluation Datasets:

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#calibration-datasetCalibration Dataset:

Link:cnn_dailymail,Nemotron-Post-Training-Dataset-v2 **Data Collection Method by dataset:**Automated. **Labeling Method by dataset:**Automated. **Properties:**The cnn_dailymail dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The Nemotron-Post-Training-Dataset-v2 is a post-training dataset curated by NVIDIA containing multi-turn conversations across diverse topics.

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#training-datasetTraining Dataset:

**Data Modality:**Undisclosed **Data Collection Method by dataset:**Undisclosed **Labeling Method by dataset:**Undisclosed **Data Size:**Undisclosed **Properties:**Undisclosed

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#evaluation-datasetEvaluation Dataset:

**Datasets:**MMLU Pro, GPQA Diamond, τ²-Bench Telecom, MMMU Pro, SciCode, AIME 2025, AA-LCR, IFBench **Data Collection Method by dataset:**Hybrid: Automated, Human **Labeling Method by dataset:**Hybrid: Human, Automated **Properties:**We evaluated the model on text-based reasoning and coding benchmarks: MMLU Pro is a multi-task language understanding benchmark with challenging multiple-choice questions across diverse academic domains; GPQA Diamond contains 448 graduate-level multiple-choice questions written by domain experts in biology, physics, and chemistry; τ²-Bench Telecom evaluates agentic tool-use and policy-adherence capabilities in dual-control telecom customer-service scenarios where the model interacts with a simulated user and external tools to resolve account issues; MMMU Pro is the more challenging version of the Massive Multi-discipline Multimodal Understanding benchmark, measuring college-level multimodal reasoning across diverse disciplines with expanded answer choices and a vision-only input setting; SciCode evaluates scientific coding capabilities; AIME 2025 contains problems from the American Invitational Mathematics Examination; AA-LCR (Artificial Analysis Long Context Recall) evaluates a model’s ability to accurately retrieve and recall information from long input contexts; IFBench is a benchmark for evaluating instruction-following capabilities across diverse and structured task constraints.

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#inferenceInference:

**Acceleration Engine:**vLLM **Test Hardware:**NVIDIA GB300

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#post-training-quantizationPost Training Quantization

This model was obtained by quantizing the weights of Qwen3.6-35B-A3B to NVFP4 data type, ready for inference with vLLM. Only the weights and activations of the linear operators within transformer blocks in MoE are quantized. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 3.06x.

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#usageUsage

To serve this checkpoint withvLLM, you can start the dockervllm/vllm\-openai:nightlyand run the sample command below:

vllm serve nvidia/Qwen3.6-35B-A3B-NVFP4 --port 8000 --quantization modelopt --max-model-len 262144 --reasoning-parser qwen3

For NVIDIA DGX Spark, we recommend setting the following environment variables and using thisvllm servecommand:

export VLLM_USE_FLASHINFER_MOE_FP4=0
export VLLM_FP8_MOE_BACKEND=flashinfer_cutlass
export FLASHINFER_DISABLE_VERSION_CHECK=1
export CUTE_DSL_ARCH=sm_121a

vllm serve nvidia/Qwen3.6-35B-A3B-NVFP4 --port 8000 --tensor-parallel-size 1 --trust-remote-code --dtype auto --quantization modelopt --kv-cache-dtype fp8 --attention-backend flashinfer --moe-backend marlin --gpu-memory-utilization 0.85 --max-model-len 65536 --max-num-seqs 4 --max-num-batched-tokens 8192 --enable-chunked-prefill --async-scheduling --enable-prefix-caching --speculative-config '{"method":"mtp","num_speculative_tokens":3,"moe_backend":"triton"}'

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#evaluationEvaluation

The accuracy benchmark results are presented in the table below:

PrecisionMMLU ProGPQA Diamondτ²-Bench TelecomSciCodeAIME 2025AA-LCRIFBenchMMMU PROBF1685.684.995.540.889.262.062.3****74.1NVFP485.084.894.740.688.862.062.8****74.5> Baseline:Qwen3.6-35B-A3B. SciCode with temperature=0.6, top_p=0.95, max num tokens 131072; the others with temperature=1.0, top_p=0.95, max num tokens 131072

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#model-limitationsModel Limitations:

The base 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. 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.

https://huggingface.co/nvidia/Qwen3.6-35B-A3B-NVFP4#ethical-considerationsEthical 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.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concernshere.