I Built an Open Source Offline ChatGPT Alternative in 40MB

— by Raj Guru Yadav Like many developers, I’ve been fascinated by LLMs. But the moment I asked: “Can I run a ChatGPT-like assistant offline, fast, and without needing 16GB+ RAM?” The challenge became too tempting to ignore.

The Goal Build a fully offline, lightweight AI assistant with:

< 50MB download size

No internet requirement

Fast responses (under 1 second)

Zero telemetry

Fully local embeddings & inference

Result: A 40MB offline ChatGPT clone you can run in-browser or on a USB stick.

What’s Inside the 40MB? Here’s how I squeezed intelligent conversation into such a tiny package:

Model: Mistral 7B Q4_K_M quantized via llama.cpp

Inference Engine: llama.cpp (compiled to WebAssembly or native C++)

UI: Lightweight React/Tailwind interface

Storage: IndexedDB for local chat history

Embeddings: Local MiniLM for smart PDF or note search

Extras: Whisper.cpp for local voice input; Coqui TTS for speech output

Why I Built It I (Raj Guru Yadav), a 16-year-old dev and student, wanted to:

Learn deeply how LLMs actually work under the hood

Build something privacy-respecting and local

Prove that AI doesn’t need the cloud to be powerful

Give offline users (like many students in India) real AI support

Challenges Memory bottlenecks in low-RAM devices

Prompt tuning for smarter replies in tiny models

WebAssembly optimizations for browser performance

Offline voice + text integration with small TTS/ASR models

Performance (on a 4GB laptop) Answers factual, coding, and math questions decently

Reads and summarizes offline PDFs

Remembers conversation locally

(Optional) Speaks answers aloud

Final Thought AI shouldn’t be locked behind paywalls or clouds. My goal is to bring smart assistants into everyone’s hands — fully offline, fully free, fully yours.

Made with by Raj Guru Yadav

Dev | Builder of 700+ projects | Passionate about open AI for all