For anyone in the self-hosted community who is tired of AI tools that claim to be "multimodal" but actually require sending your private data and documents to third-party cloud APIs, this project is for you.
A few months ago, a post on Reddit went viral where a 19-year-old student from Bihar announced he was building a 5.82-billion-parameter multimodal AI model all on his own. Very few people believed it was possible, and an anonymous user even bought a typo domain to set up a smear site called "The Dossier," claiming the project was a total fraud.
Instead of arguing with anonymous critics, I kept my head down, turned 20, and focused entirely on building and shipping.
Hi — I am that boy. My name is Abhinav Anand, and I am from Bihar.
Today, I am officially releasing Arcle V1 — an open-weight, 5.84-billion-parameter unified omni foundation model designed specifically to run self-hosted and offline.
WHY THIS MATTERS FOR SELF-HOSTING: NO API WRAPPERS, NO CLOUD MESH
Most "multimodal" setups in the self-hosted space today are orchestration nightmares: you have to run a text LLM, attach an external OCR microservice, bridge a separate Whisper container, wire a TTS server, and glue them together with an API router.
Arcle V1 eliminates that complexity entirely:
Single Neural Network Module: Text, images, scanned documents, speech, and audio all project into one shared 2,560-dimensional semantic latent space.
Single Forward Pass: Everything runs through one model file and one set of weights. It is not an API wrapper, not a router, and not a pipeline.
100% Offline & Private: Verified with HF_HUB_OFFLINE=1. It requires zero internet connection, makes zero external cloud calls, and sends zero telemetry. Your data never leaves your hardware.
Ultra-Long Context: Architectural context window of 2,097,152 tokens (2 million tokens).
7 Native Capabilities: Conversational reasoning, coding, multi-step math, 512×512 original image generation, complex document reading, speech recognition, and 24kHz natural speech output.
Created, Built, Trained, and Tested from Bihar.
STANDARDIZED BENCHMARK EVALUATIONS
Evaluated across standard academic benchmarks against leading open and on-device models in the 3B-4B class:
ARC-Easy (Grade-School Science): 80.0%
GSM8K (Multi-step Math Reasoning): 77.5%
MATH-500 (Complex Mathematics): 74.2%
HellaSwag (Commonsense Reasoning): 67.0%
BBH (Multi-step Logic): 53.7%
TruthfulQA (Factual Truthfulness): 53.2%
ARC-Challenge (Hard Science): 48.5%
MMLU (General Multi-domain Knowledge): 43.5%
Document OCR (Synthetic & Scanned Forms): 94.6% content-word recall
THE PEOPLE BEHIND THE MODEL
Building an open foundation model under extremely limited resources is tough. Arcle V1 exists today because two people believed in a teenager when skeptics did not:
Neil Bhatt sir (VP of Product at Lightning AI) and the entire Lightning AI team: Neil sir believed in a young builder from Bihar and provided the critical compute resources required to train the model. Without their faith and infrastructure, Arcle V1 would simply not exist.
Abhinav Kumar Singh: My best friend and brother from another mother. He took on the massive responsibility of curating, filtering, and structuring our training datasets. The reasoning density and benchmark scores carry his fingerprints as much as mine.
THE ROAD AHEAD: ARCLE V2
Arcle V1 is our foundational milestone. For Arcle V2, we are actively engineering:
Native Video Generation: Full architectural integration of text-to-video and image-to-video generation within the omni graph.
Multi-Voice Expressive Speech: Dynamic vocal synthesis capturing natural emotions across multiple expressive voices.
Higher Benchmark Scores: Significant leaps across advanced mathematical logic and scientific problem-solving.
Hardened Cybersecurity Capabilities: Built-in vulnerability detection, automated code auditing, and secure inference defenses.
Extreme On-Device Efficiency: High-throughput inference optimized specifically for local consumer hardware and homelab setups.
HOW THE COMMUNITY CAN SUPPORT US
Building sovereign, open-source AI without corporate venture capital is a community mission. To bring Arcle V2 to life, we welcome community collaboration:
Compute & Fund Contributions: Every contribution goes 100% directly toward GPU compute clusters to power the Arcle V2 training runs.
Data Contributions (Codebases, Technical PDFs, Books): Some AI monopolies scan rare historical books and proprietary literature, locking human heritage behind expensive subscription paywalls. Our objective is to preserve and democratize this knowledge. If you have technical PDFs, codebases, or rare literature scans, consider donating them to our pipeline. Our commitment: Any proprietary data you contribute will remain strictly private, heavily anonymized, securely processed, and will never be sold or shared with any commercial AI entity.
They said a lone boy from Bihar couldn't build a real omni foundation model. An anonymous website claimed I had built nothing at all.
Let's prove, together, that the self-hosted and open-source community can build something greater than any centralized corporation.
Download the weights, run it locally, test it, benchmark it, and let me know your thoughts and hardware performance in the comments!
— Abhinav Anand
Founder, ArcleIntelligence
https://i.redd.it/7i1c7nu1tboh1.jpeg
Source: r/selfhosted · by /u/arcleintelligence