What are the hardware requirements for Qwen2.5 7B Instruct GPTQ Int4?

Qwen2.5 7B Instruct GPTQ Int4 requires approximately 5.6 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 7B Instruct GPTQ Int4 and how large is it?

Qwen2.5 7B Instruct GPTQ Int4 is developed by Qwen, a model with 7b 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 7B Instruct GPTQ Int4?

Qwen2.5 7B Instruct GPTQ Int4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 7B Instruct GPTQ Int4 by Qwen — VRAM 5.6GB, 32K context

Name: Qwen2.5 7B Instruct GPTQ Int4
Author: Qwen

Qwen2.5 7B Instruct GPTQ Int4 is an open-source language model by Qwen. Features: 7b LLM, VRAM: 5.6GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.22.

Arxiv:2309.00071 Arxiv:2407.10671 4-bit Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-7b-ins... Chat Conversational Deploy:azure En Endpoints compatible Gptq Instruct Quantized Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4

Qwen2.5 7B Instruct GPTQ Int4 Parameters and Internals

Model Type

Causal Language Model

Use Cases

Areas:

Research, Commercial applications

Supported Languages

English (Proficient), Chinese (Proficient), French (Proficient), Spanish (Proficient), Portuguese (Proficient), German (Proficient), Italian (Proficient), Russian (Proficient), Japanese (Proficient), Korean (Proficient), Vietnamese (Proficient), Thai (Proficient), Arabic (Proficient)

Training Details

Methodology:

Pretraining & Post-training

Context Length:

131072

Model Architecture:

Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias

Input Output

Accepted Modalities:

Text

Output Format:

Text

Performance Tips:

Add `rope_scaling` configuration only when processing long contexts.

LLM Name	Qwen2.5 7B Instruct GPTQ Int4
Repository 🤗	https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4
Base Model(s)	Qwen/Qwen2.5-7B-Instruct Qwen/Qwen2.5-7B-Instruct
Model Size	7b
Required VRAM	5.6 GB
Updated	2026-07-20
Maintainer	Qwen
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.0 GB: 1-of-2 1.6 GB: 2-of-2
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	152064
Torch Data Type	float16
Errors	replace

Rank the Qwen2.5 7B Instruct GPTQ Int4 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 7B Instruct GPTQ Int4

Best Alternatives	Context / RAM	Downloads	Likes
....5 Coder 7B Instruct GPTQ Int4	32K / 5.6 GB	509821	14
Qwen2.5 7B Instruct GPTQ Int8	32K / 8.9 GB	64136	18
....5 Coder 7B Instruct GPTQ Int8	32K / 8.9 GB	1475	5
Qwen2 7B Instruct GPTQ Int8	32K / 8.9 GB	1646	17
Qwen2 7B Instruct GPTQ Int4	32K / 5.6 GB	546	28
Qwen2.5 7B Instruct 1M 4bit	986K / 4.3 GB	444	11
...wen2.5 7B Instruct 1M Bnb 4bit	986K / 5.5 GB	267	1
Qwen2.5 7B Instruct 1M 8bit	986K / 8.1 GB	63	4
Qwen2.5 7B Instruct 1M 6bit	986K / 6.2 GB	52	2
...B Instruct 1M Unsloth Bnb 4bit	986K / 7.5 GB	37	3

Note: green Score (e.g. "73.2") means that the model is better than Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4.