What are the hardware requirements for Nanomath?

Nanomath requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Nanomath and how large is it?

Nanomath is developed by rekhi. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

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

Name: Nanomath
Author: rekhi

Nanomath is an open-source language model by rekhi. Features: LLM, VRAM: 0.2GB, License: mit, LLM Explorer Score: 0.32.

LLM Name	Nanomath
Repository 🤗	https://huggingface.co/rekhi/nanomath
Required VRAM	0.2 GB
Updated	2026-08-25
Maintainer	rekhi
Model Files	0.2 GB
Supported Languages	en
License	mit
Is Biased	0
Vocabulary Size	98

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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