What are the hardware requirements for Qwen2.5 0.5B Instruct GPTQ Int8?

Qwen2.5 0.5B Instruct GPTQ Int8 requires approximately 0.6 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 0.5B Instruct GPTQ Int8 and how large is it?

Qwen2.5 0.5B Instruct GPTQ Int8 is developed by Qwen, a model with 0.5b 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 Qwen2.5 0.5B Instruct GPTQ Int8?

Qwen2.5 0.5B Instruct GPTQ Int8 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 0.5B Instruct GPTQ Int8 by Qwen — VRAM 0.6GB, 32K context

Name: Qwen2.5 0.5B Instruct GPTQ Int8
Author: Qwen

Qwen2.5 0.5B Instruct GPTQ Int8 is an open-source language model by Qwen. Features: 0.5b LLM, VRAM: 0.6GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.15.

Arxiv:2407.10671 8-bit Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-0.5b-i... Chat Conversational En Endpoints compatible Gptq Instruct Quantized Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8

Qwen2.5 0.5B Instruct GPTQ Int8 Parameters and Internals

Model Type

Causal Language Models

Additional Notes

The Qwen2.5 model offers significant improvements in coding and mathematics, long-context support, multilingual capabilities, and enhanced instruction-following.

Supported Languages

en (English), zh (Chinese), fr (French), es (Spanish), pt (Portuguese), de (German), it (Italian), ru (Russian), ja (Japanese), ko (Korean), vi (Vietnamese), th (Thai), ar (Arabic)

Training Details

Methodology:

Pretraining & Post-training

Context Length:

32768

Model Architecture:

transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings

LLM Name	Qwen2.5 0.5B Instruct GPTQ Int8
Repository 🤗	https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8
Base Model(s)	Qwen/Qwen2.5-0.5B-Instruct Qwen/Qwen2.5-0.5B-Instruct
Model Size	0.5b
Required VRAM	0.6 GB
Updated	2026-07-31
Maintainer	Qwen
Model Type	qwen2
Instruction-Based	Yes
Model Files	0.6 GB
Supported Languages	en
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.39.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float16
Errors	replace

Rank the Qwen2.5 0.5B Instruct GPTQ Int8 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 Qwen2.5 0.5B Instruct GPTQ Int8

Best Alternatives	Context / RAM	Downloads	Likes
...wen2.5 0.5B Instruct GPTQ Int4	32K / 0.5 GB	1901	9
... Coder 0.5B Instruct GPTQ Int4	32K / 0.5 GB	321	1
Qwen2 0.5B Instruct GPTQ Int4	32K / 0.7 GB	5365	15
... Coder 0.5B Instruct GPTQ Int8	32K / 0.6 GB	108	1
Qwen2Vanilla GPTQ 4bits	32K / 0.7 GB	5	0
Qwen2Vanilla GPTQ 8bits	32K / 0.9 GB	5	0
Qwen2 0.5B Instruct GPTQ Int8	32K / 1.5 GB	48	4
Pgabl Ft Nama Siswa	32K / 1 GB	253	0
Legal Rag Qwen Sft	32K / 1 GB	204	0
Atomight V2.2 UltraThink 0.5B	32K / 1 GB	54	1

Note: green Score (e.g. "73.2") means that the model is better than Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8.