The Opportunity in AI Model Training
The market for AI model training, specifically in the area of Large Language Models (LLMs) post-training, is substantial and rapidly growing. Today, this market is valued at $2-4 billion, with projections indicating a significant surge to $12-20 billion by 2030. Gradients are positioned at the core of this expanding opportunity, driving advancements that make AI smarter, faster, and more accessible for a wide range of businesses and individual users.
Key Achievements of Gradients
- •Over 2,900 jobs have been successfully completed since the platform's launch.
- •More than 200 individual miners are actively competing on a global scale.
- •Scripts developed through the Gradients platform have demonstrated an average performance boost of 45%.
- •A remarkable 52% of users become repeat customers after their initial engagement.
- •Gradients boasts a 100% success rate in tests, outperforming major AI platforms like HuggingFace and Google Cloud.
The LLM post-training market: $2-4B today, $12-20B by 2030.
Enterprises face an impossible choice: hire ML engineers at $150k-500k each, or accept mediocre AutoML results.
Gradients solves this.
Full breakdown doc hits in 4 days. https://t.co/E2cIWdGkgYpic.twitter.com/OsHibfymi9
— Gradients (@gradients_ai) November 6, 2025
The Challenge in Custom AI Model Development
Developing custom AI models traditionally comes with significant costs:
- •Hiring top-tier machine learning engineers can range from $150,000 to $500,000 per person annually.
- •While AutoML platforms offer lower-cost options, they often provide generic, "one-size-fits-all" solutions that lack the sophistication for specific needs. Gradients addresses this gap with its innovative competitive tournament model.
For AI enthusiasts and data scientists, participation in these mining tournaments offers the opportunity to win cash prizes and build professional credibility.
$9-30 billion spent annually on ML engineers doing fine-tuning work.
We automate it at 60-80% cost reduction.
With 11-42% better performance.
The labor replacement opportunity is massive, and we’re just getting started.
Full breakdown 👇https://t.co/yO8k0BiGAs
— Gradients (@gradients_ai) November 16, 2025
How Gradients Operates
Developed by Rayon Labs, Gradients' flagship product utilizes an innovative approach:
- •AI "miners" submit their training scripts to the platform.
- •These scripts then compete through a series of group rounds, knockout stages, and a final challenge.
- •The scripts that emerge as winners are made open-source and are used to power all client AI jobs.
- •This model ensures that everyone benefits from the most effective and high-performing AI solutions.
At @proofoftalk Const has just revealed his new dTAO subnet: Affine (sn120).
Affine will use Celium (sn56), Gradients (sn120) and Chutes (sn64). I believe this will be the holy trio for the next time. I’ve made some rebalances into them today.$TAOpic.twitter.com/D0msUjxc44
— Zubi (@Zubi5566) June 10, 2025
The Significance of Decentralized AI Model Training
- •Enterprises can achieve annual savings ranging from $20,000 to over $1 million by transitioning from expensive custom-built solutions to Gradients.
- •Startups can reduce costs by $10,000 to $20,000 per year while obtaining superior AI quality compared to standard AutoML services.
- •Users benefit from access to open and transparent AI code, avoiding the limitations of proprietary models controlled by large technology corporations.
An example of an image generated by training a Gradients Model is available for review.
Concluding Remarks
Gradients is in its early stages of development, with models continuously being trained and refined. While it is not yet fully ready for broad retail use, such as direct image generation, the platform is actively perfecting its capabilities. The ultimate success of this project hinges on widespread participation from individuals and developers.
Participation is still possible, but it requires the use of TAO tokens for training. Further details on this aspect will be provided in upcoming articles.
