What are the hardware requirements for Qwen3.8 27B Growpath?

Qwen3.8 27B Growpath requires approximately 54.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B Growpath and how large is it?

Qwen3.8 27B Growpath is developed by Ssum-mer, 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 Growpath?

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

Qwen3.8 27B Growpath by Ssum-mer — VRAM 54.7GB

Name: Qwen3.8 27B Growpath
Author: Ssum-mer

Qwen3.8 27B Growpath is an open-source language model by Ssum-mer. Features: 27b LLM, VRAM: 54.7GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.31.

Base model:finetune:qwen/qwen3... Base model:qwen/qwen3.8-27b Conversational En Endpoints compatible Finetuned Gguf Growpath Image-text-to-text Ko Korean Lora Merge Quantized Qwen3.8 Qwen3 5 Region:us Roadmap-generation Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Ssum-mer/qwen3.8-27b-growpath

Qwen3.8 27B Growpath Parameters and Internals

LLM Name	Qwen3.8 27B Growpath
Repository 🤗	https://huggingface.co/Ssum-mer/qwen3.8-27b-growpath
Model Name	GrowPath (Qwen3.8-27B)
Base Model(s)	Qwen/Qwen3.8-27B Qwen/Qwen3.8-27B
Model Size	27b
Required VRAM	54.7 GB
Updated	2026-10-31
Maintainer	Ssum-mer
Model Type	qwen3_5
Model Files	2.5 GB: 1-of-12 4.8 GB: 2-of-12 5.0 GB: 3-of-12 4.9 GB: 4-of-12 5.0 GB: 5-of-12 4.9 GB: 6-of-12 4.9 GB: 7-of-12 5.0 GB: 8-of-12 5.0 GB: 9-of-12 4.9 GB: 10-of-12 5.0 GB: 11-of-12 2.8 GB: 12-of-12 53.8 GB
Supported Languages	ko en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.21.0
Tokenizer Class	Qwen3_5Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.8 27B Growpath 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 Growpath

Best Alternatives	Context / RAM	Downloads	Likes
...N V1.1 NM DAU NEO MAX MTP GGUF	0K / 0.9 GB	421538	328
Qwen3.8	0K / 0.9 GB	54	1
... Opus Reasoning Distilled GGUF	0K / 0.9 GB	67921	679
Qwen3.8 27B GGUF	0K / 0.9 GB	0	685
Qwen3.8 27B OBLITERATED	0K / 83.2 GB	1989	2
...B Abliterated V2 PQ2 0 MTP MLX	0K / 0.8 GB	3224	7
...easoning Instruct MTP MLX 4bit	0K / 16.6 GB	366	2
...N Blackwell DFlash2 Ultra V1.0	0K / 0.9 GB	3531	3
Swift Qwen3.8 27B MIX GGUF	0K / 0.9 GB	2219	2
...inkingCap Qwen3.8 27B MIX GGUF	0K / 13.4 GB	1395	7

Note: green Score (e.g. "73.2") means that the model is better than Ssum-mer/qwen3.8-27b-growpath.