What are the hardware requirements for GRaPE 2.5 Quasar?

GRaPE 2.5 Quasar requires approximately 55.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GRaPE 2.5 Quasar and how large is it?

GRaPE 2.5 Quasar is developed by SL-AI, a model with 9b 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 GRaPE 2.5 Quasar?

GRaPE 2.5 Quasar is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GRaPE 2.5 Quasar by SL-AI — VRAM 55.6GB

Name: GRaPE 2.5 Quasar
Author: SL-AI

GRaPE 2.5 Quasar is an open-source language model by SL-AI. Features: 9b LLM, VRAM: 55.6GB, License: apache-2.0.

Ar Base model:finetune:qwen/qwen3... Base model:qwen/qwen3.5-9b Chat Coding Conversational Creative writing De En Endpoints compatible Es Fr Grape Image-text-to-text Instruct Ja Ko Math Multimodal Pt Qwen3.5 Qwen3 5 Reasoning Region:us Roleplay Ru Safetensors Science Sharded Tensorflow Thinking modes Vision Zh

Model Card on HF 🤗: https://huggingface.co/SL-AI/GRaPE-2.5-Quasar

GRaPE 2.5 Quasar Parameters and Internals

LLM Name	GRaPE 2.5 Quasar
Repository 🤗	https://huggingface.co/SL-AI/GRaPE-2.5-Quasar
Base Model(s)	Qwen/Qwen3.5-9B Qwen/Qwen3.5-9B
Model Size	9b
Required VRAM	55.6 GB
Updated	2026-09-12
Maintainer	SL-AI
Model Type	qwen3_5
Model Files	4.0 GB: 1-of-18 3.0 GB: 2-of-18 2.5 GB: 3-of-18 4.0 GB: 4-of-18 2.1 GB: 5-of-18 4.0 GB: 6-of-18 2.1 GB: 7-of-18 4.0 GB: 8-of-18 2.1 GB: 9-of-18 4.0 GB: 10-of-18 2.1 GB: 11-of-18 4.0 GB: 12-of-18 2.1 GB: 13-of-18 4.0 GB: 14-of-18 2.1 GB: 15-of-18 4.0 GB: 16-of-18 2.1 GB: 17-of-18 3.4 GB: 18-of-18
Supported Languages	en zh fr de es ja ko pt ru ar
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 GRaPE 2.5 Quasar 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 GRaPE 2.5 Quasar

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.8 Distilled 9B NPU2	256K / GB	3611	3
Qwen3.5 9B	0K / 19.3 GB	12207758	1799
Qwable 9B Claude Fable 5	0K / 18.8 GB	1088	115
Ornith 1.0 9B	0K / 18.8 GB	1263936	541
Qwythos 9B Claude Mythos 5 1M	0K / 18.8 GB	37427	906
Negentropy Claude Opus 4.7 9B	0K / 18.8 GB	63	26
Qwen3.8 9B Distill	0K / 19.3 GB	5839	148
WeMM Embedding 9B	0K / 18.8 GB	2618	105
OxCoder 9B	0K / 18.8 GB	357	28
Ornith 1.5 9B	0K / 18.9 GB	1324	120

Note: green Score (e.g. "73.2") means that the model is better than SL-AI/GRaPE-2.5-Quasar.