What are the hardware requirements for Qwopus3.8 27B Flash V2 4bit?

Qwopus3.8 27B Flash V2 4bit requires approximately 16 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 4bit and how large is it?

Qwopus3.8 27B Flash V2 4bit is developed by mlx-community, 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 4bit?

Qwopus3.8 27B Flash V2 4bit 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 4bit by mlx-community — VRAM 16GB

Name: Qwopus3.8 27B Flash V2 4bit
Author: mlx-community

Qwopus3.8 27B Flash V2 4bit is an open-source language model by mlx-community. Features: 27b LLM, VRAM: 16GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.29.

4-bit 4bit Agent Base model:jackrong/qwopus3.8-... Base model:quantized:jackrong/... Coder Conversational En Es Function-calling Image-text-to-text Ja Long-context Mlx Moe Multimodal Quantized Qwen3 5 Qwen3 5 moe Qwen3 6 Region:us Ru Safetensors Sharded Tensorflow Tool-use Video Vision Zh

Model Card on HF 🤗: https://huggingface.co/mlx-community/Qwopus3.8-27B-Flash-V2-4bit

Qwopus3.8 27B Flash V2 4bit Parameters and Internals

LLM Name	Qwopus3.8 27B Flash V2 4bit
Repository 🤗	https://huggingface.co/mlx-community/Qwopus3.8-27B-Flash-V2-4bit
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	16 GB
Updated	2026-09-29
Maintainer	mlx-community
Model Type	qwen3_5
Model Files	5.3 GB: 1-of-3 5.3 GB: 2-of-3 5.4 GB: 3-of-3
Supported Languages	en zh es ru ja
Quantization Type	4bit
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwopus3.8 27B Flash V2 4bit 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 4bit

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.6 27B OptiQ 4bit	256K / 18.7 GB	13065	59
Bonsai 27B Mlx 1bit	0K / 5.1 GB	809309	205
Ternary Bonsai 27B Mlx 2bit	0K / 8.5 GB	809024	168
Qwen3.8 27B MLX 5bit	0K / 19.5 GB	4675044	2
Qwen3.6 27B MLX 8bit	0K / 29.4 GB	879145	14
Qwen3.6 27B MLX 6bit	0K / 22.7 GB	812977	2
...onsai 2 27B CRACK Ternary JANG	0K / 8.1 GB	2689	13
Qwen3.6 27B MLX 4bit	0K / 16 GB	846861	16
...ngCap Qwen3.8 27B MLX 4bit DWQ	0K / 21.1 GB	674	9
Qwen3.8 27B 8bit	0K / 29.4 GB	13892	33

Note: green Score (e.g. "73.2") means that the model is better than mlx-community/Qwopus3.8-27B-Flash-V2-4bit.