EvolAI is Bittensor subnet 47 (SN47), a project in the Model Fine-Tuning lane. Decentralizing the LLM benchmark.
Miners train language models and publish them to HuggingFace. Validators score them on-chain. Best model wins the rewards.
It is an educational deep dive, not a recommendation to stake. For live price, pool depth, and performance, always open the live subnet page.
What is EvolAI (SN47)?
EvolAI (SN47) is a Bittensor subnet for competitive language model training. Miners train their own LLMs and publish them publicly on HuggingFace. Validators then evaluate those models and assign scores that determine reward distribution.
In plain English: It's like Kaggle for language models, but on a blockchain. Train a model, post it publicly, get scored by the network, earn rewards based on how your model performs.
Centralized reference points: Hugging Face Open LLM Leaderboard, Kaggle
How miners and validators work
Like every Bittensor subnet, SN47 is a two-sided market: miners produce work, validators score it, and emissions follow those weights under the network’s dTAO economics.
- Miners train language models using either a Transformer or Mamba2 architecture, publish them publicly on HuggingFace with "evolai" in the name, and register them on the subnet with a wallet and hotkey. Miners re-register each time they publish an updated model.
- Validators evaluate submitted models. The validator software requires a GPU with 80GB VRAM to run evaluations.
Mechanism and design
According to the subnet's public repository, EvolAI runs two parallel tracks: transformer and mamba2. Miners choose a track, train a model, and upload it to HuggingFace as a public checkpoint. The model name must contain "evolai" to be eligible. Once uploaded, miners register their model on the subnet by specifying their wallet, hotkey, and track. They then receive a challenge from a validator to complete registration. After each new model version is published, miners re-register. Validators fetch the submitted models and run evaluations, requiring 80GB of GPU VRAM to do so.
The subnet's name and framing, "evolving AI systems," points to a design intent around iterative improvement: miners are pushed by the reward structure to continuously update and improve their models rather than coasting on a one-time submission.
EvolAI is thin and early. The root-side pool holds around 1,189 TAO, with a root proportion of 50.5%, meaning roughly half the pool depth comes from protocol subsidy rather than organic demand. Price discovery is still in progress. The subnet holds 0.63% of total network emissions. Daily buy volume has been around 421 TAO against sell volume of ~365 TAO, giving a positive net on 24-hour flow. Chain buys sit at 0.47% of emissions, a separate metric from the emission share itself and not a risk indicator at this level.
The single GitHub contributor and 31-commit codebase reflect very early stage development. The last commit was two days ago, suggesting active work. No website, Discord, or Twitter handle appeared in the on-chain identity, which limits community discovery at this point.
Why the project exists
- The problem it solves: Centralized LLM leaderboards are run by single teams. That creates a trust gap: who picks the benchmark tasks, who can game the eval? EvolAI moves that scoring process onto an incentivized validator network.
- The opportunity: Open model evaluation is still a largely unsolved problem. A neutral, economically-incentivized benchmark could matter as the open-source LLM ecosystem scales.
- The Bittensor advantage: Validators are staked participants with skin in the game. Dishonest scoring costs them. Miner rewards track directly to model quality, which creates continuous competitive pressure to improve.
- Traction signals: The subnet is early. GitHub shows 31 commits from a single contributor, with the most recent commit just two days ago. No official website, Discord, or Twitter has been published on-chain. Price has moved sharply positive, up 31% over 7 days and 29% over 30 days, with net TAO inflows over the past week positive at around 160 TAO.
What stakers should check before buying alpha
Subnet storytelling is cheap. On-chain and product checks are not. Use the same framework as our subnet metrics guide:
| Check | Why |
|---|---|
| Real product / docs / repo | Separates shipping teams from narrative-only slots |
| Pool depth (TAO) | Your exit door: thin pools amplify slippage |
| Net flows (7d / 30d) | Under flow-based emissions, capital votes with its feet |
| Emission share | Whether the market is funding this subnet right now |
| Ownership concentration | Large holders can move a small alpha market |
| Immunity / deregistration | Post-immunity weak slots can lose the race (see immunity guide) |
Pull the live numbers on EvolAI · SN47 rather than trusting any static screenshot in a blog post.
Risk factors
- Thin liquidity: The root-side pool holds around 1,189 TAO. Slippage on entry or exit will be material, and a single large position can move the alpha price significantly.
- Development concentration: One contributor on GitHub means key-person risk. If the developer steps back, development stalls with no visible backup.
- No public community presence: No official website, Discord, or social accounts have been registered on-chain. Discovery is limited to on-chain data and secondary aggregators.
- Early pool, high root proportion: Root prop at 50.5% means the pool is still in early price discovery and has not yet established deep organic demand. Price action here can be volatile relative to larger subnets.
- Undocumented evaluation methodology: The public repository describes how to submit and register models, but does not detail the specific evaluation criteria or benchmark tasks validators use. This opacity is a risk for miners trying to optimize their training and for anyone assessing the system's integrity.
How this fits a TAO portfolio
Many holders keep a root vs subnet split: root for calmer TAO exposure, subnets like EvolAI for thematic upside (and higher variance). If you use TaoScale, you can size SN47 from your wallet without handing over custody. See non-custodial staking.
- Live market: SN47 on TaoScale
- Browse and stake: /invest
- More subnet explainers: /blog/subnets
FAQ
What does EvolAI do on Bittensor?
It's like Kaggle for language models, but on a blockchain. Train a model, post it publicly, get scored by the network, earn rewards based on how your model performs.
What is the SN47 alpha token?
When you stake TAO into subnet 47, you receive that subnet’s alpha. Its price versus TAO moves with the subnet’s pool (see what is dTAO).
How do I research EvolAI before staking?
Read the mechanism above, open the official repo/docs when available, then verify pool depth, flows, and activity on SN47. Cross-check with the metrics checklist.
Is this financial advice?
No. TaoScale publishes educational explainers and live tools. Decisions to stake are yours alone.
Not financial advice. Educational overview of EvolAI (SN47) compiled from public project materials and ecosystem documentation. Mechanisms and market structure change, so verify live data and official sources before acting.
