What are the hardware requirements for Swift Qwen3.8 27B OQ4e Fp16 Mtp?

Swift Qwen3.8 27B OQ4e Fp16 Mtp requires approximately 17.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Swift Qwen3.8 27B OQ4e Fp16 Mtp and how large is it?

Swift Qwen3.8 27B OQ4e Fp16 Mtp is developed by Yanun, 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 Swift Qwen3.8 27B OQ4e Fp16 Mtp?

Swift Qwen3.8 27B OQ4e Fp16 Mtp is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Swift Qwen3.8 27B OQ4e Fp16 Mtp by Yanun — VRAM 17.8GB

Name: Swift Qwen3.8 27B OQ4e Fp16 Mtp
Author: Yanun

Swift Qwen3.8 27B OQ4e Fp16 Mtp is an open-source language model by Yanun. Features: 27b LLM, VRAM: 17.8GB, License: other, Quantized, LLM Explorer Score: 0.37.

4-bit Base model:quantized:ukisai/sw... Base model:ukisai/swift-qwen3.... Conversational Fp16 Image-text-to-text Mlx Mtp Omlx Quantized Qwen3 5 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Yanun/Swift-Qwen3.8-27b-oQ4e-fp16-mtp

Swift Qwen3.8 27B OQ4e Fp16 Mtp Parameters and Internals

LLM Name	Swift Qwen3.8 27B OQ4e Fp16 Mtp
Repository 🤗	https://huggingface.co/Yanun/Swift-Qwen3.8-27b-oQ4e-fp16-mtp
Base Model(s)	ukisai/Swift-Qwen3.8-27b ukisai/Swift-Qwen3.8-27b
Model Size	27b
Required VRAM	17.8 GB
Updated	2026-09-16
Maintainer	Yanun
Model Type	qwen3_5
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 2.8 GB: 4-of-4
Quantization Type	fp16
Model Architecture	Qwen3_5ForConditionalGeneration
License	other
Model Max Length	262144
Transformers Version	5.8.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Swift Qwen3.8 27B OQ4e Fp16 Mtp 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 Swift Qwen3.8 27B OQ4e Fp16 Mtp

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
Qwen3.8 27B 8bit	0K / 29.4 GB	13892	33
Qwen3.6 27B MLX 4bit	0K / 16 GB	846861	16
Qwen3.8 27B MLX 8bit	0K / 29.4 GB	24811	7
Qwen3.8 27B MLX 4bit	0K / 16 GB	19712	10

Note: green Score (e.g. "73.2") means that the model is better than Yanun/Swift-Qwen3.8-27b-oQ4e-fp16-mtp.