What are the hardware requirements for Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090?

Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090 requires approximately 17.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090?

Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090 by heswithme — VRAM 17.9GB

Name: Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090
Author: heswithme

Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090 is an open-source language model by heswithme. Features: 27b LLM, VRAM: 17.9GB, License: apache-2.0, LLM Explorer Score: 0.31.

8-bit Abliterated Base model:gittensor-model-hub... Base model:quantized:gittensor... Conversational Dspark Endpoints compatible Image-text-to-text Modelopt Nvfp4 Qwen3.8 Qwen3 5 Region:us Rtx-5090 Safetensors Sglang Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/heswithme/Huihui-Qwen3.8-27B-Abliterated-Gittensor-Style-NVFP4-RTX5090

Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090 Parameters and Internals

LLM Name	Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090
Repository 🤗	https://huggingface.co/heswithme/Huihui-Qwen3.8-27B-Abliterated-Gittensor-Style-NVFP4-RTX5090
Base Model(s)	huihui-ai/Huihui-Qwen3.8-27B-abliterated gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 huihui-ai/Huihui-Qwen3.8-27B-abliterated gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090
Model Size	27b
Required VRAM	17.9 GB
Updated	2026-09-04
Maintainer	heswithme
Model Type	qwen3_5
Model Files	10.0 GB: 1-of-2 7.9 GB: 2-of-2
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.12.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090 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 Huihui Qwen3.8 27B Abliterated Gittensor Style NVFP4 RTX5090

Best Alternatives	Context / RAM	Downloads	Likes
Agens Pilot	1024K / 54.7 GB	87	1
Qwen3.6 27B	0K / 55.4 GB	6728138	2197
Qwen3.8 27B MLX	0K / 16 GB	1411	18
Qwen3.8 27B FP8	0K / 0.4 GB	123157	416
Qwen3.8 27B	0K / 55.6 GB	2727	21
Qwen3.8 27B	0K / 55.6 GB	2	8753
Qwen3.6 27B NVFP4	0K / 22 GB	1887525	419
Qwen3.6 27B FP8	0K / 0.4 GB	7740472	342
Cortex Mini 1 Preview	0K / 55.6 GB	40	2
Qwen3.5 27B	0K / 55.2 GB	2536294	1028

Note: green Score (e.g. "73.2") means that the model is better than heswithme/Huihui-Qwen3.8-27B-Abliterated-Gittensor-Style-NVFP4-RTX5090.