What are the hardware requirements for Qwen3.8 27B Pi 4bit Mtp Mlx?

Qwen3.8 27B Pi 4bit Mtp Mlx requires approximately 15.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B Pi 4bit Mtp Mlx and how large is it?

Qwen3.8 27B Pi 4bit Mtp Mlx is developed by leonsarmiento, 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 Pi 4bit Mtp Mlx?

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

Qwen3.8 27B Pi 4bit Mtp Mlx by leonsarmiento — VRAM 15.8GB

Name: Qwen3.8 27B Pi 4bit Mtp Mlx
Author: leonsarmiento

Qwen3.8 27B Pi 4bit Mtp Mlx is an open-source language model by leonsarmiento. Features: 27b LLM, VRAM: 15.8GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.31.

4-bit 4bit Base model:bytkim/qwen3.8-27b-... Base model:quantized:bytkim/qw... Coding Conversational Image-text-to-text Mlx Mlx-vlm Mtp Quantized Qwen3 Qwen3.8 Qwen3 5 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/leonsarmiento/Qwen3.8-27B-pi-4bit-mtp-mlx

Qwen3.8 27B Pi 4bit Mtp Mlx Parameters and Internals

LLM Name	Qwen3.8 27B Pi 4bit Mtp Mlx
Repository 🤗	https://huggingface.co/leonsarmiento/Qwen3.8-27B-pi-4bit-mtp-mlx
Base Model(s)	bytkim/Qwen3.8-27B-pi bytkim/Qwen3.8-27B-pi
Model Size	27b
Required VRAM	15.8 GB
Updated	2026-10-05
Maintainer	leonsarmiento
Model Type	qwen3_5
Model Files	5.3 GB: 1-of-3 5.3 GB: 2-of-3 5.2 GB: 3-of-3
Quantization Type	4bit
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.16.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.8 27B Pi 4bit Mtp Mlx 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 Pi 4bit Mtp Mlx

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
Qwen3.6 27B MLX 4bit	0K / 16 GB	846861	16
Qwen3.8 27B 8bit	0K / 29.4 GB	13892	33
...onsai 2 27B CRACK Ternary JANG	0K / 8.1 GB	2689	13
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 leonsarmiento/Qwen3.8-27B-pi-4bit-mtp-mlx.