What are the hardware requirements for Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16?

Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16 requires approximately 19.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16 and how large is it?

Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16 is developed by bjonor, 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 GPTQ Int4 Sym G128 MTP BF16?

Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16 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 GPTQ Int4 Sym G128 MTP BF16 by bjonor — VRAM 19.6GB

Name: Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16
Author: bjonor

Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16 is an open-source language model by bjonor. Features: 27b LLM, VRAM: 19.6GB, License: other, Quantized, LLM Explorer Score: 0.29.

4-bit Arc-pro-b70 Base model:quantized:ukisai/sw... Base model:ukisai/swift-qwen3.... Conversational Dataset:huggingfaceh4/ultracha... En Endpoints compatible Gated-deltanet Gptq Image-text-to-text Int4 Intel-xpu Mtp Multilingual Quantized Qwen3 5 Region:us Safetensors Sharded Speculative-decoding Tensorflow Tool-calling Vllm W4a16

Model Card on HF 🤗: https://huggingface.co/bjonor/Swift-Qwen3.8-27B-GPTQ-Int4-sym-G128-MTP-BF16

Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16 Parameters and Internals

LLM Name	Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16
Repository 🤗	https://huggingface.co/bjonor/Swift-Qwen3.8-27B-GPTQ-Int4-sym-G128-MTP-BF16
Base Model(s)	ukisai/Swift-Qwen3.8-27b ukisai/Swift-Qwen3.8-27b
Model Size	27b
Required VRAM	19.6 GB
Updated	2026-09-20
Maintainer	bjonor
Model Type	qwen3_5
Model Files	3.5 GB: 1-of-5 4.3 GB: 2-of-5 4.3 GB: 3-of-5 4.3 GB: 4-of-5 3.2 GB: 5-of-5
Supported Languages	en
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	Qwen3_5ForConditionalGeneration
License	other
Model Max Length	262144
Transformers Version	5.17.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16 capabilities

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Best Alternatives to Swift Qwen3.8 27B GPTQ Int4 Sym G128 MTP BF16

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 G32	0K / 21 GB	2134	3
Qwen3.8 27B GPTQ INT4 G128	0K / 19.6 GB	2010	3
...7B GPTQ Int4 Sym G128 MTP BF16	0K / 19.6 GB	322	1
Qwen3.8 27B GPTQ INT8	0K / 31.9 GB	1042	2

Note: green Score (e.g. "73.2") means that the model is better than bjonor/Swift-Qwen3.8-27B-GPTQ-Int4-sym-G128-MTP-BF16.