What are the hardware requirements for QwentileSwap?

QwentileSwap requires approximately 65 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed QwentileSwap and how large is it?

QwentileSwap is developed by Aryanne, a model with 32b 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 QwentileSwap?

QwentileSwap is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

QwentileSwap by Aryanne — VRAM 65GB, 32K context

Name: QwentileSwap
Author: Aryanne

QwentileSwap is an open-source language model by Aryanne. Features: 32b LLM, VRAM: 65GB, Context: 32K, License: apache-2.0, Quantized, Merged, LLM Explorer Score: 0.29.

Merged Model Base model:arliai/qwen2.5-32b-... Base model:maldv/qwentile2.5-3... Base model:sao10k/32b-qwen2.5-... Base model:win10/eva-qwq-32b-p... Conversational Deploy:azure Endpoints compatible Gguf Quantized Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Aryanne/QwentileSwap

QwentileSwap Parameters and Internals

LLM Name	QwentileSwap
Repository 🤗	https://huggingface.co/Aryanne/QwentileSwap
Base Model(s)	EVA QwQ 32B Preview ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3 maldv/Qwentile2.5-32B-Instruct Sao10K/32B-Qwen2.5-Kunou-v1 win10/EVA-QwQ-32B-Preview ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3 maldv/Qwentile2.5-32B-Instruct Sao10K/32B-Qwen2.5-Kunou-v1
Merged Model	Yes
Model Size	32b
Required VRAM	65 GB
Updated	2026-08-24
Maintainer	Aryanne
Model Type	qwen2
Model Files	1.6 GB: 1-of-23 2.8 GB: 2-of-23 2.9 GB: 3-of-23 2.9 GB: 4-of-23 2.9 GB: 5-of-23 2.9 GB: 6-of-23 2.9 GB: 7-of-23 2.9 GB: 8-of-23 2.9 GB: 9-of-23 2.9 GB: 10-of-23 2.9 GB: 11-of-23 2.9 GB: 12-of-23 2.9 GB: 13-of-23 2.9 GB: 14-of-23 2.9 GB: 15-of-23 2.9 GB: 16-of-23 2.9 GB: 17-of-23 2.9 GB: 18-of-23 2.9 GB: 19-of-23 2.9 GB: 20-of-23 2.9 GB: 21-of-23 2.9 GB: 22-of-23 2.6 GB: 23-of-23 18.8 GB
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.48.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the QwentileSwap 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 QwentileSwap

Best Alternatives	Context / RAM	Downloads	Likes
JiRackUltra 32b	128K / 12.3 GB	4487	0
QwQ 32B GGUF	32K / 7.7 GB	3819	86
WPAIGPT Fse Patterns 1	32K / 65.8 GB	111	3
...Logic Mix 3 32B Mlx OptiQ 5bit	128K / 22.3 GB	41	1
...pSeek R1 Distill Qwen 32B 4bit	128K / 18.5 GB	7795	51
Hydraulic Deepseek 16bit	128K / 65.8 GB	52	0
...k R1 Distill Qwen 32B Bnb 4bit	128K / 19.2 GB	2717	29
...till Qwen 32B Abliterated 4bit	128K / 18.5 GB	888	7
QwQ 32B Unsloth Bnb 4bit	128K / 22.5 GB	179	47
...ogito V1 Preview Qwen 32B 4bit	128K / 18.5 GB	43	5

Note: green Score (e.g. "73.2") means that the model is better than Aryanne/QwentileSwap.