What are the hardware requirements for Qwen3.6 35B A3B AntiLoop NVFP4?

Qwen3.6 35B A3B AntiLoop NVFP4 requires approximately 21.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.6 35B A3B AntiLoop NVFP4 and how large is it?

Qwen3.6 35B A3B AntiLoop NVFP4 is developed by mlx-community, a model with 35b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Qwen3.6 35B A3B AntiLoop NVFP4?

Qwen3.6 35B A3B AntiLoop NVFP4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.6 35B A3B AntiLoop NVFP4 by mlx-community — VRAM 21.3GB

Name: Qwen3.6 35B A3B AntiLoop NVFP4
Author: mlx-community

Qwen3.6 35B A3B AntiLoop NVFP4 is an open-source language model by mlx-community. Features: 35b LLM, VRAM: 21.3GB, License: apache-2.0, LLM Explorer Score: 0.31.

Antidoom Base model:adapter:n8programs/... Base model:n8programs/qwen3.6-... Conversational Fp4 Fp8 Lora Merge Mlx Mlx-lm Modelopt Moe Nvfp4 Quantized Qwen3.6 Qwen3 5 moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mlx-community/Qwen3.6-35B-A3B-AntiLoop-NVFP4

Qwen3.6 35B A3B AntiLoop NVFP4 Parameters and Internals

LLM Name	Qwen3.6 35B A3B AntiLoop NVFP4
Repository 🤗	https://huggingface.co/mlx-community/Qwen3.6-35B-A3B-AntiLoop-NVFP4
Base Model(s)	nvidia/Qwen3.6-35B-A3B-NVFP4 N8Programs/Qwen3.6-35B-A3B-AntiLoop nvidia/Qwen3.6-35B-A3B-NVFP4 N8Programs/Qwen3.6-35B-A3B-AntiLoop
Model Size	35b
Required VRAM	21.3 GB
Updated	2026-08-05
Maintainer	mlx-community
Model Type	qwen3_5_moe
Model Files	1.3 GB: 1-of-41 0.5 GB: 2-of-41 0.5 GB: 3-of-41 0.5 GB: 4-of-41 0.5 GB: 5-of-41 0.5 GB: 6-of-41 0.5 GB: 7-of-41 0.5 GB: 8-of-41 0.5 GB: 9-of-41 0.5 GB: 10-of-41 0.5 GB: 11-of-41 0.5 GB: 12-of-41 0.5 GB: 13-of-41 0.5 GB: 14-of-41 0.5 GB: 15-of-41 0.5 GB: 16-of-41 0.5 GB: 17-of-41 0.5 GB: 18-of-41 0.5 GB: 19-of-41 0.5 GB: 20-of-41 0.5 GB: 21-of-41 0.5 GB: 22-of-41 0.5 GB: 23-of-41 0.5 GB: 24-of-41 0.5 GB: 25-of-41 0.5 GB: 26-of-41 0.5 GB: 27-of-41 0.5 GB: 28-of-41 0.5 GB: 29-of-41 0.5 GB: 30-of-41 0.5 GB: 31-of-41 0.5 GB: 32-of-41 0.5 GB: 33-of-41 0.5 GB: 34-of-41 0.5 GB: 35-of-41 0.5 GB: 36-of-41 0.5 GB: 37-of-41 0.5 GB: 38-of-41 0.5 GB: 39-of-41 0.5 GB: 40-of-41 0.5 GB: 41-of-41
Model Architecture	Qwen3_5MoeForConditionalGeneration
License	apache-2.0
Transformers Version	5.7.0.dev0

Rank the Qwen3.6 35B A3B AntiLoop NVFP4 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Qwen3.6 35B A3B AntiLoop NVFP4

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.6 35B A3B	0K / 72.2 GB	5540855	2648
KAT Coder V2.5 Dev	0K / 69.7 GB	17885	537
Qwen3.6 35B A3B NVFP4	0K / 23.4 GB	8788684	482
XYZ Aquila Mini	0K / 70.2 GB	1760	432
BTL 4	0K / 69.9 GB	571	74
Qwen3.6 35B A3B Escha W2	0K / 12.3 GB	3622	236
Qwen3.6 35B A3B Abliterated V2	0K / 70.2 GB	621	9
BigBang V1	0K / 71.5 GB	154	84
...e 4.7 Opus Reasoning Distilled	0K / 72.2 GB	28687	194
GRM 3.2 Sky	0K / 70.4 GB	717	31

Note: green Score (e.g. "73.2") means that the model is better than mlx-community/Qwen3.6-35B-A3B-AntiLoop-NVFP4.