MEXC adds Bittensor TAO staking via Yuma validator
MEXC integrated Yuma’s validator to let about 40 million users stake Bittensor’s TAO, which delegates rewards across 128 specialized subnets.
Cryptocurrency exchange MEXC has enabled staking of Bittensor’s native TAO token by integrating validator Yuma, making the validator available to the exchange’s reported 40 million users.
Yuma connected its validator infrastructure to MEXC on Tuesday, allowing users to delegate TAO through the exchange. The validator takes part in Bittensor’s consensus by measuring subnet performance and assigning weights that influence how staking rewards are distributed.
Bittensor operates a network of 128 specialized subnets, each providing specific AI functions such as inference, model training, code assistance and financial modeling. On Bittensor, token holders delegate TAO to validators rather than staking directly to the protocol.
Validators allocate delegated stake across competing subnets and earn rewards based on those allocations and measured subnet contributions. The integration lets MEXC users delegate without running their own validator nodes, using the exchange’s staking interface.
The companies described the integration as intended to expand access to the Bittensor ecosystem and increase participation in the network. Yuma’s assessments and allocation choices determine how rewards flow to delegators and subnets.
Interest in decentralized AI and open model development has risen as regulators review access to large models. The US Commerce Department restricted public access to certain Anthropic models on national security and export control grounds.
At the time of reporting, TAO traded around $199, giving the token a market capitalization near $1.916 billion, according to CoinMarketCap. MEXC reported roughly 40 million users; Yuma announced the integration on Tuesday.
The integration reduces technical barriers for retail holders by enabling delegation through MEXC. Validator evaluation quality and allocation strategy directly affect how much reward delegators receive.
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