Pioneering Tomorrow with Decentralized AI
Introducing a World of Truly Open, Cheap and Scalable AI (Bitcoin for AI). A World built on Bittensor Blockchain for Monetising, Mining & Inventing Open Source AI models
About Us
An ecosystem built on Bittensor Blockchain focussing on Mining cutting edge Open Source Decentralised AI Models (Subnets) profitably and inventing new ones. These subnets can be launched globally, are supported by a miner community for compute requirements and are cheaper on average than centralised models.
Read MoreOur Services
In the world of decentralised AI we provide end to end service on bittensor and working to make decentralised AI more accessible and affordable.
Mining Subnets
Selecting Subnets with least competition (more recently launched), purchasing NVIDIA chips, developing unique algorithms for improving each subnet. Testing TAO Emissions, Electricity & Cooling needs, 24&7 invigilation. Mining at scale.
Retail Mining
Selling assembled Hardware ( NVIDIA chips, Motherboard etc.) with technology support for in-house mining to clients via monthly retainers. Helping generate consistent cashflow. Near $0.5 Million sales generated for Ethereum Mining previously.
Inventing Subnets
Mining Subnets and launching new AI models via accelerator yumaai.com backed by Billionaire Bitcoin evangelist Barry Silbert. 18% continuous Emission Rewards are shared with Subnet Owners for simply offering the service to the Bittensor network.
Why Decentralized AI?
Centralized AI models dominate today’s landscape, but they come with significant drawbacks. At Tapovan AI, we believe decentralized AI—powered by the Bittensor Blockchain—offers a superior alternative, addressing the limitations of traditional systems and unlocking a world of possibilities. Here’s why:
Biased & Not Open Sourced
Models from Google, Meta, OpenAI, and others are not fully open source, embedding biases that limit performance and true adaptability.
Pricing
The centralized nature of these entities drives higher pricing, reinforced by their monopolistic control over the market.
Expensive Maintenance
Centralized ownership and maintenance of computing chips result in larger upkeep costs, passed on to users.
Barriers to Innovation
High compute requirements create barriers, restricting the ability for anyone to innovate on large global AI models.
Why on Bittensor?
Tapovan AI builds on the Bittensor Blockchain—the "Bitcoin for AI"—to deliver decentralized, open-source intelligence with unmatched potential. Bittensor’s ecosystem offers exponential growth opportunities, industry recognition, and a competitive edge over centralized AI giants. Here’s why we choose Bittensor:
Expected 5X-10X price growth in TAO token in 5 years, with previous 80X growth in the last 3 years. Total: 1500*0.65% - 3000*0.65% growth expected. 10X to 20X return expected on capital over 5 years.
How Big Tech, ChatGPT, and DeepSeek Could Lose to Decentralized AI Bittensor - Forbes, Feb 2025
Decentralised AI - MIT Media Labs, May 2024
Digital Currency Group’s Billionaire Barry Silbert Bets Big on AI Blockchain Bittensor - CoinDesk, Nov 2024
Decentralized AI Opportunity Is 'Bigger than Bitcoin,' Says DCG's Barry Silbert - CoinDesk, Feb 2025
Market Opportunity
The decentralized AI landscape, powered by Bittensor, presents a transformative market opportunity. From liquid returns to trillion-dollar potential, Tapovan AI is positioned to capitalize on this explosive growth. Explore the possibilities driving our vision:
Liquid Mining Returns
Mining is a cash-rich business with investor returns liquid in TAO token via Binance exchange. No long-term conviction in TAO is needed for short-term exit.
Subnet Innovation
Creating high-quality new Subnets (AI Models) offers higher return potential. Subnet Owners earn 18% continuous emission rewards for offering services to Bittensor.
Trillion-Dollar Market
Bain suggests AI is a trillion-dollar opportunity by 2027. A 10% penetration implies 50X+ growth potential for short-term (5-year) TAO token holding.
Bitcoin-Scale Potential
Bitcoin evangelist Barry Silbert (Grayscale, DCG) suggests Bittensor could surpass Bitcoin’s $2T market cap, hinting at 1000X TAO growth in a decade.
Models
Explore how Bittensor’s decentralized AI models compare to centralized giants. With superior speed, cost-efficiency, and scalability, Tapovan AI leverages Bittensor to bridge the gap and redefine intelligence.
Large Language Models
| Model | Type | Speed (tokens/sec) | Cost ($) | Insight |
|---|---|---|---|---|
| ChatGPT (GPT-4 Turbo) | Cloud API | ~40-80 tps (real-time UI) | 0.4-mini (cheapest model) ~ $1.2/M tokens | High accuracy. Slower |
| Grok (xAI) | Web UI | 1200 tokens per second for Llama 3 8B | $2/M tokens | Very sparse data. Not Open |
| Gemini 1.5 | Cloud API | ~50-100 tps | $0.3-0.6/M tokens | Good for long context, free tier basic only |
| Targon Bittensor (Subnet 4) | Backend | 2286 tokens per second for Llama 3 8B * | $0.04/M tokens | Very cheap, raw speed |
| NineteenAI Bittensor (Subnet 19) | Local/Free | 300 tps | Free | Great for beginner & basic use |
* Conclusion: For less complicated tasks, Bittensor AI models should gain market traction as they are cheaper and faster. Centralized AI models with larger data sets generate more accurate results slowly. As Bittensor models get access to more data, this gap could easily be bridged.
Large Language Model Network Model Visualization
Image Generation Models
Image Generation Model Visualization
| Model | Daily Image Generation Capacity | Quality | Insight |
|---|---|---|---|
| SocialTensor (Zuvu) (Subnet 23) Bittensor | >21 million images | Medium | Emphasizes throughput over stylization. |
| DALL·E 2 | >2 million images (as of 2022) | High | Developed by OpenAI, generates diverse and high-quality images from textual descriptions. |
| Midjourney | ~2.5 million images (as of 2022) | High | Known for artistic and stylized image generations, operates via Discord interface. |
* Conclusion: Bittensor Image Generation Models have medium quality on average with current data. It should gain Market Traction due to 10 times higher image generation capacity currently.
Finetuning Models
| Platform | Avg. Accuracy on Standard Datasets | OpenML Scoreboard Rank | Notable Strengths |
|---|---|---|---|
| Gradients (Subnet 56) Bittensor | Best | Unknown | Claimed state-of-the-art AutoML capabilities |
| AutoGlucon | 78% - 86% | Top 3-5 | Strong tabular data performance |
| Autosklean | 75% - 83% | Top 5-10 | Scikit-learn based, easy to integrate |
| H2O AutoML | 76%–85% | Top 5–10 | Great on structured/tabular data |
| TPOT | 70%–80% | Mid tier | Interpretable pipelines |
| Google AutoML | 85%–90% | Not Open-Source | High accuracy, limited transparency |
* Conclusion: Bittensor finetuning model Gradients claims to be state of the art beating other models on cost, performance and ease of use by far. Graphs available for verification. Market Traction expected in the niche.
Finetuning Model Visualization
Deepfake AI Detection
Image Generation Model Visualization
| Model | Leaderboard Rank | Detection Accuracy | Domain | Strengths |
|---|---|---|---|---|
| It's AI (Subnet 32) Bittensor | #1 on RAID | 92% on RAID 98% on GRiD | Multi-modal AI detection | Best-in-class on RAID benchmark |
| DeepFakeDet | Top 3 | 91–95% on video/image | Deepfake videos | Open source, fast |
| DetectGPT | Mid–High | ~85–90% on GPT outputs | AI-written text | Text-only detection |
| OpenAI Detector | Deprecated | ~80–85% on GPT-2/3 | AI-written text | Limited to specific models |
| FakeCatcher (Intel) | High (External tests) | 96% (claimed) | Real-time video | Real-time physiological signal-based |
* Conclusion: Bittensor Deepfake AI Detection models are leading in comparison and should gain market traction. However, it is generally getting harder to detect AI.
Agentic AI Models
| Model | Task Performance | Agentic Ability | Integration |
|---|---|---|---|
| Apex (SN 1) | High 22%+ better than LLaMA 3.1 70B Model | High – Fully Agentic | Open (LLM-agnostic) |
| AutoGPT | Medium – task completion varies | Medium – goal-based | Python-based CLI or API |
| LangGraph | High (when backed by strong LLMs) | High – graph logic agents | LangChain ecosystem |
| SWE-Agent (Meta) | Very High (code benchmarks) | Medium – task-specific | Custom evals (e.g., coding tasks) |
| OpenDevin | High (dev task focused) | High – simulates dev work | Open-source full-stack setup |
* Conclusion: Bittensor Apex's Agentic workflow model leads on performance, beating top centralised models like LLaMA 3.1 (Meta 70 Billion Parameters) by over 22%. Since Agentic models do more than answer questions, by completing tasks, they may become more used than standard AI models Expected High Market Traction growth.
Agentic AI Models Model Visualization
Roadmap
Our roadmap is a blueprint for growth, blending innovation with execution. we are strategically building a robust ecosystem for mining, retail solutions, and Subnet innovation on the Bittensor Blockchain.
2025 : Ideation & Foundation
The journey begins with ideation and in depth research on the subnets of bittensor.
2025 : Pilot Mining
We launch our pilot phase, mining high-performing Subnets and creating a credible knowledge base to mine on multiple networks.
2025 : Retail Mining Launch
We roll out retail mining operations, selling assembled hardware with technical support via monthly retainers.
2026: Series A & Large-Scale Mining
Raising Series A funding, we expand to large-scale mining in the Himalayas, utilizing natural cooling and low electricity costs to enhance profitability.
2027-2029: Innovation & Excellence
With Series B and C funding, we invent new Subnets, refine mining efficiency, and aim to be a global leader by 2029, managing multiple AI models.
Team
Our success is driven by a team of exceptional individuals who bring together decades of expertise in blockchain, software development, and entrepreneurial leadership. Each member contributes unique skills and insights, ensuring we stay ahead in a rapidly evolving landscape.
Rishabh Kapoor
Co-Founder, ManagementInvestor, IIT Bombay, Delhi, Kharagpur awarded & exited startup, TU Delft, Netherlands Bitcoin Master's Thesis
Ashish Kumar
Co-Founder, TechnologyCo-Founder & CEO at Codenia Technologies with 20 + Years of Experience in Software & Mobile Apps with leading brands
Anant Singh
Business AdvisorIIT Bombay, ISB, Info Edge, Mu Sigma, Shopclues. Ecommerce Entreprenur at Weshow.Live with Successful Exit & Advisor
Nadeem Khan
Software DeveloperSenior Software Developer with near 15 Years of experience. Java, Mobile Application, DevOps, IOT, Blockchain and Web Development
Arpit Chauhan
Software DeveloperFull Stack Developer with near 5 Years Experience with Blockchain, React.Js, Node.Js, AWS, MongoDB Figma, MERN Stack
Arnav Bhatt
AI/ML InternIndian Institute of Science (IISC) Student focussed on AI, Machine Learning and Deep Learning. Java, Python
Investment Proposal
Tapovan AI is spearheading the decentralized AI revolution, and we’re inviting forward-thinking investors to join us in this transformative journey. We’re poised to capture a significant share of the trillion-dollar AI market by 2027. Our seed funding round offers a rare opportunity to invest in a company with a clear roadmap to profitability, a competitive edge over centralized AI.
Use of Funds
Mining Operations :
40% - Deploy NVIDIA H100 and A6000 chips for pilot Subnet mining and scale to 70+ Subnets by 2026 in the Himalayas.-
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Retail Mining :
30% - Manufacture and sell assembled hardware with technical support, building on our Ethereum success. -
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Subnet Innovation :
20% - Partner with Yumq.ai to invent new AI models, securing 18% emission rewards as Subnet owners. -
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Team & Operations :
10% - Expand our core team and cover operational costs to ensure seamless execution. -
Contact
Have questions or ready to join our decentralized AI journey? We’d love to hear from you. Reach out to discuss investment opportunities, partnerships, or simply to learn more about Tapovan AI. Connect with us today!
Address
C-51, Sixth Floor, Sector 62, Noida, Uttar Pradesh 201301, India