What are the hardware requirements for GPT Neo 2.7B?

GPT Neo 2.7B requires approximately 10.7 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GPT Neo 2.7B and how large is it?

GPT Neo 2.7B is developed by EleutherAI, a model with 2.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does GPT Neo 2.7B perform on standard benchmarks?

GPT Neo 2.7B has the following published scores: MMLU 26.45. Compare against reference models on this page or on the LLM Explorer leaderboards.

GPT Neo 2.7B by EleutherAI — VRAM 10.7GB, 2K context

Name: GPT Neo 2.7B
Author: EleutherAI

GPT Neo 2.7B is an open-source language model by EleutherAI. Features: 2.7b LLM, VRAM: 10.7GB, Context: 2K, License: mit, LLM Explorer Score: 0.12, Arc: 33.4, HellaSwag: 56.2, MMLU: 26.5, GSM8K: 1.3.

Arxiv:2101.00027 Dataset:eleutherai/pile Deploy:azure En Endpoints compatible Gpt neo Jax Pytorch Region:us Rust Safetensors

Model Card on HF 🤗: https://huggingface.co/EleutherAI/gpt-neo-2.7B

GPT Neo 2.7B Parameters and Internals

Model Type

text generation, causal-lm

Use Cases

Areas:

research, commercial applications

Applications:

text generation, language processing

Primary Use Cases:

generating texts from a prompt

Limitations:

unknown outputs, socially unacceptable text potential

Considerations:

Intent to improve results with human curation/filtering.

Supported Languages

English (proficient)

Training Details

Data Sources:

EleutherAI/pile

Data Volume:

420 billion tokens

Methodology:

masked autoregressive

Model Architecture:

transformer

Responsible Ai Considerations

Fairness:

depends on Pile's dataset content

Transparency:

unknown biases in training data

Accountability:

Users are advised to curate outputs

Mitigation Strategies:

Human curation recommended

Input Output

Input Format:

text prompt

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Use text-generation pipeline for generation

LLM Name	GPT Neo 2.7B
Repository 🤗	https://huggingface.co/EleutherAI/gpt-neo-2.7B
Model Size	2.7b
Required VRAM	10.7 GB
Updated	2026-07-12
Maintainer	EleutherAI
Model Type	gpt_neo
Model Files	10.7 GB 10.7 GB
Supported Languages	en
Model Architecture	GPTNeoForCausalLM
License	mit
Context Length	2048
Model Max Length	2048
Transformers Version	4.9.0.dev0
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	50257
Activation Function	gelu_new

Rank the GPT Neo 2.7B 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 GPT Neo 2.7B

Best Alternatives	Context / RAM	Downloads	Likes
ChildModel 02	2K / 5.3 GB	8	0
GPT Neo 2.7B Lama	2K / 10.6 GB	5	0
PRIME2 Openai	2K / 6.6 GB	4	1
EleutherAI GPT Neo 2.7B 4bits	2K / 1.7 GB	9	0
Pygmalion 2.7B	2K / 5.4 GB	214	59
... Style Transfer Using Examples	2K / 5.4 GB	5	1
Pygmalion 2.7B	2K / 0 GB	3	1
GPT Neo 2.7B Horni	2K / 6.6 GB	7	0
GPT Neo 2.7B Shinen	2K / 6.6 GB	5	0
GPT Neo 2.7B Horni LN	2K / 6.6 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than EleutherAI/gpt-neo-2.7B.