What are the hardware requirements for Gpt4all J?

Gpt4all J requires approximately 12.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gpt4all J and how large is it?

Gpt4all J is developed by nomic-ai, a model with 6b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gpt4all J perform on standard benchmarks?

Gpt4all J has the following published scores: MMLU 29.03. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gpt4all J by nomic-ai — VRAM 12.2GB

Name: Gpt4all J
Author: nomic-ai

Gpt4all J is an open-source language model by nomic-ai. Features: 6b LLM, VRAM: 12.2GB, License: apache-2.0, LLM Explorer Score: 0.11, Arc: 41.3, HellaSwag: 64.5, MMLU: 29, GSM8K: 9.3.

Dataset:nomic-ai/gpt4all-j-pro... En Endpoints compatible Gptj Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/nomic-ai/gpt4all-j

Gpt4all J Parameters and Internals

Model Type

text generation, chatbot

Use Cases

Areas:

Research, Commercial Applications

Supported Languages

English (NLP)

Training Details

Data Sources:

nomic-ai/gpt4all-j-prompt-generations

Methodology:

finetuning from GPT-J on assistant style interaction data

Training Time:

~12 hours

Hardware Used:

DGX cluster with 8 A100 80GB GPUs

Model Architecture:

GPT-J finetuned

Release Notes

Version:

v1.0

Notes:

The original model trained on the v1.0 dataset.

Version:

v1.1-breezy

Notes:

Trained on filtered dataset removing instances of 'AI language model'.

Version:

v1.2-jazzy

Notes:

Trained on further filtered dataset, removing specific phrases.

Version:

v1.3-groovy

Notes:

Incorporated Dolly and ShareGPT data, filtered duplicate semantic data.

LLM Name	Gpt4all J
Repository 🤗	https://huggingface.co/nomic-ai/gpt4all-j
Model Size	6b
Required VRAM	12.2 GB
Updated	2026-07-31
Maintainer	nomic-ai
Model Type	gptj
Model Files	10.0 GB: 1-of-2 2.2 GB: 2-of-2 12.2 GB: 1-of-2 12.1 GB: 2-of-2
Supported Languages	en
Model Architecture	GPTJForCausalLM
License	apache-2.0
Model Max Length	2048
Transformers Version	4.28.0.dev0
Tokenizer Class	GPT2Tokenizer
Beginning of Sentence Token	<\|endoftext\|>
End of Sentence Token	<\|endoftext\|>
Unk Token	<\|endoftext\|>
Vocabulary Size	50400
Torch Data Type	bfloat16
Activation Function	gelu_new
Errors	replace

Rank the Gpt4all J capabilities

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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 Gpt4all J

Best Alternatives	Context / RAM	Downloads	Likes
Mlperf GPT J 6B	0K / 24.1 GB	11595	0
GPT J 6B	0K / 24.2 GB	246404	1526
Deception Normal	0K / 12.2 GB	8	0
Deception Filteredpositive	0K / 12.2 GB	7	0
Pygmalion 6B	0K / 16.3 GB	286	753
Pygmalion 6B Roleplay	0K / 12.1 GB	782	2
Gptj Allenai Toxicity Blackbox	0K / 12.2 GB	9	0
...j Allenai Toxicity Explainable	0K / 12.2 GB	10	0
Test GPT J 6B	0K / 2.5 GB	7	0
Explainable GPT J 6B	0K / 24.2 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than nomic-ai/gpt4all-j.