What are the hardware requirements for Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB?

Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB requires approximately 0.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB and how large is it?

Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB is developed by JRamirez-UAB, 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 Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB?

Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB by JRamirez-UAB — VRAM 0.8GB

Name: Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB
Author: JRamirez-UAB

Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB is an open-source language model by JRamirez-UAB. Features: 27b LLM, VRAM: 0.8GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.28.

Ampere Base model:jackrong/qwopus3.8-... Base model:quantized:jackrong/... Compressed-tensors Conversational Dataset:huggingfaceh4/ultracha... Endpoints compatible Gptq Image-text-to-text Int4 Long-context Mtp Quantized Qwen3.8 Qwen3 5 Qwopus Region:us Rtx3090 Safetensors Speculative-decoding Vllm W4a16

Model Card on HF 🤗: https://huggingface.co/JRamirez-UAB/Qwopus3.8-27B-Flash-V2-GPTQ-W4A16-embed-int4-24GB

Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB Parameters and Internals

LLM Name	Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB
Repository 🤗	https://huggingface.co/JRamirez-UAB/Qwopus3.8-27B-Flash-V2-GPTQ-W4A16-embed-int4-24GB
Model Name	/home/llm/.cache/huggingface/hub/models--unsloth--Qwen3.8-27B/snapshots/3ea932cee0a432ae86e9c7826cbe8aef52323a28
Base Model(s)	Jackrong/Qwopus3.8-27B-Flash-V2 Jackrong/Qwopus3.8-27B-Flash-V2
Model Size	27b
Required VRAM	0.8 GB
Updated	2026-10-07
Maintainer	JRamirez-UAB
Model Type	qwen3_5
Model Files	14.8 GB 0.8 GB
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>
Torch Data Type	bfloat16
Errors	replace

Rank the Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB 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 Qwopus3.8 27B Flash V2 GPTQ W4A16 Embed Int4 24GB

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.6 27B GPTQ Pro 4bit	0K / 18.8 GB	415407	39
Qwen3.8 27B GPTQ INT4 FP8KV	0K / 20.2 GB	6488	2
Qwen3.5 27B GPTQ Int4	0K / 30.1 GB	105381	57
...en3.8 27B W4A16 AutoRound GPTQ	0K / 19.2 GB	3748	5
Qwen3.8 27B GPTQ 4bit	0K / 21 GB	2433	5
Qwen3.8 27B GPTQ Int4	0K / 20.2 GB	2267	3
Qwen3.8 27B GPTQ INT4 G128	0K / 19.6 GB	2045	3
...7B GPTQ Int4 Sym G128 MTP BF16	0K / 19.6 GB	175	3
... GPTQ Int4 Baked V1 Embed Int8	0K / 16.2 GB	39	4
Qwen3.8 27B GPTQ Int4	0K / 19.6 GB	881	1

Note: green Score (e.g. "73.2") means that the model is better than JRamirez-UAB/Qwopus3.8-27B-Flash-V2-GPTQ-W4A16-embed-int4-24GB.