grail Price (SN81)

The live grail (SN81) price today is $ 2.47, with a 8.42% change over the past 24 hours. The current SN81 to USD conversion rate is $ 2.47 per SN81.
grail currently ranks #- by market capitalization at $ 6,371,269, with a circulating supply of 2.58M SN81. During the last 24 hours, SN81 traded between $ 2.46 (low) and $ 2.75 (high), reflecting market activity. Its all-time high stands at $ 7.51, while the all-time low was $ 1.92.
In short-term performance, SN81 moved 0.00% in the last hour and -14.85% over the past 7 days. Over the past day, total trading volume reached --.
The current Market Cap of grail is $ 6.37M, with a 24-hour trading volume of --. The circulating supply of SN81 is 2.58M, with a total supply of 2577791.448663103. Its Fully Diluted Valuation (FDV) is $ 6.37M.
0.00%
-8.41%
-14.85%
-14.85%
During today, the price change of grail to USD was $ -0.22718783950088.
In the past 30 days, the price change of grail to USD was $ -0.7130544200.
In the past 60 days, the price change of grail to USD was $ -0.8391207500.
In the past 90 days, the price change of grail to USD was $ -2.560823581826629.
| Period | Change (USD) | Change (%) |
|---|---|---|
| Today | $ -0.22718783950088 | -8.41% |
| 30 Days | $ -0.7130544200 | -28.86% |
| 60 Days | $ -0.8391207500 | -33.97% |
| 90 Days | $ -2.560823581826629 | -50.90% |
In 2040, the price of grail could potentially see a growth of 0.00%. It could reach a trading price of $ --.
Grail operates as subnet 81 on the Bittensor network, serving as the reinforcement learning fine-tuning and post-training layer of the Covenant AI ecosystem. The project implements the GRAIL protocol (Guaranteed Rollout Authenticity via Inference Ledger) to provide cryptographically verifiable model outputs during reinforcement learning rollouts. Grail transforms foundation models from pre-training subnets into deployable, aligned AI systems through permissionless, incentivized RL fine-tuning infrastructure. The platform generates thousands of rollouts per second using custom algorithms designed for post-training optimization. The subnet supports models ranging from 8B to 70B+ parameters and integrates with multiple RL environments for diverse training scenarios.
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