What are the hardware requirements for Qwen3.8 27B Abliterated MTPLX 4bit?

Qwen3.8 27B Abliterated MTPLX 4bit requires approximately 16.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B Abliterated MTPLX 4bit and how large is it?

Qwen3.8 27B Abliterated MTPLX 4bit is developed by grant-ai, 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 Abliterated MTPLX 4bit?

Qwen3.8 27B Abliterated MTPLX 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B Abliterated MTPLX 4bit by grant-ai — VRAM 16.6GB

Name: Qwen3.8 27B Abliterated MTPLX 4bit
Author: grant-ai

Qwen3.8 27B Abliterated MTPLX 4bit is an open-source language model by grant-ai. Features: 27b LLM, VRAM: 16.6GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.32.

4-bit 4bit Abliterated Apple-silicon Base model:blackfrost-ai/qwen3... Base model:quantized:blackfros... Conversational Image-text-to-text Mlx Mtp Mtplx Quantized Qwen3.8-27b Qwen3 5 Region:us Safetensors Sharded Speculative-decoding Tensorflow Vision Vision-language

Model Card on HF 🤗: https://huggingface.co/grant-ai/Qwen3.8-27B-Abliterated-MTPLX-4bit

Qwen3.8 27B Abliterated MTPLX 4bit Parameters and Internals

LLM Name	Qwen3.8 27B Abliterated MTPLX 4bit
Repository 🤗	https://huggingface.co/grant-ai/Qwen3.8-27B-Abliterated-MTPLX-4bit
Base Model(s)	Blackfrost-AI/Qwen3.8-27B-ABLITERATED-BF16 Qwen/Qwen3.8-27B Blackfrost-AI/Qwen3.8-27B-ABLITERATED-BF16 Qwen/Qwen3.8-27B
Model Size	27b
Required VRAM	16.6 GB
Updated	2026-08-28
Maintainer	grant-ai
Model Type	qwen3_5
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 1.6 GB: 4-of-4 0.3 GB
Quantization Type	4bit
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.8.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.8 27B Abliterated MTPLX 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 Qwen3.8 27B Abliterated MTPLX 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
...en3.8 27B Abliterated MLX 4bit	0K / 16 GB	1664	26
Qwen3.8 27B 8bit	0K / 29.4 GB	13892	33
Qwen3.6 27B MLX 8bit	0K / 29.4 GB	879145	14
Qwen3.8 27B 4bit	0K / 16 GB	6055	27
Qwen3.8 27B MLX 4bit	0K / 16 GB	19712	10
Qwen3.6 27B MLX 6bit	0K / 22.7 GB	812977	2
Qwen3.8 27B MLX 8bit	0K / 29.4 GB	24811	7

Note: green Score (e.g. "73.2") means that the model is better than grant-ai/Qwen3.8-27B-Abliterated-MTPLX-4bit.