What are the hardware requirements for Qwen3.8 27B NVFP4 RTX5090 LMHead4?

Qwen3.8 27B NVFP4 RTX5090 LMHead4 requires approximately 18.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B NVFP4 RTX5090 LMHead4 and how large is it?

Qwen3.8 27B NVFP4 RTX5090 LMHead4 is developed by gittensor-model-hub, a model with 27b 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 Qwen3.8 27B NVFP4 RTX5090 LMHead4?

Qwen3.8 27B NVFP4 RTX5090 LMHead4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B NVFP4 RTX5090 LMHead4 by gittensor-model-hub — VRAM 18.8GB

Name: Qwen3.8 27B NVFP4 RTX5090 LMHead4
Author: gittensor-model-hub

Qwen3.8 27B NVFP4 RTX5090 LMHead4 is an open-source language model by gittensor-model-hub. Features: 27b LLM, VRAM: 18.8GB, License: apache-2.0, LLM Explorer Score: 0.32.

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

Model Card on HF 🤗: https://huggingface.co/gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090-LMHead4

Qwen3.8 27B NVFP4 RTX5090 LMHead4 Parameters and Internals

LLM Name	Qwen3.8 27B NVFP4 RTX5090 LMHead4
Repository 🤗	https://huggingface.co/gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090-LMHead4
Base Model(s)	gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090
Model Size	27b
Required VRAM	18.8 GB
Updated	2026-08-24
Maintainer	gittensor-model-hub
Model Type	qwen3_5
Model Files	10.0 GB: 1-of-3 8.1 GB: 2-of-3 0.7 GB: 3-of-3
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.9.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.8 27B NVFP4 RTX5090 LMHead4 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 Qwen3.8 27B NVFP4 RTX5090 LMHead4

Best Alternatives	Context / RAM	Downloads	Likes
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
Qwen3.6 27B	0K / 55.4 GB	6728138	2197
Qwen3.8 27B	0K / 55.6 GB	2	8753
Qwen3.6 27B NVFP4	0K / 22 GB	1887525	419
ThinkingCap Qwen3.6 27B	0K / 54.7 GB	35947	614
Qwen3.6 27B FP8	0K / 0.4 GB	7740472	342
Qwen3.5 27B	0K / 55.2 GB	2536294	1028
Qwen3.8 27B Escha W2	0K / 10.1 GB	561	97

Note: green Score (e.g. "73.2") means that the model is better than gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090-LMHead4.