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New Solana Vaults Use AI to Chase 15% Yields on SOL

Twitter icon  •  Published il y a 1 heure on August 11, 2026  •  Hassan Maishera

Project 0 and GammaFi have launched machine-learning DeFi vaults that automatically optimize stablecoin, SOL, and liquid staking token yields on Solana.

New Solana Vaults Use AI to Chase 15% Yields on SOL

TL;DR

  • Project 0 has partnered with GammaFi to launch machine-learning-powered DeFi vaults.

  • The vaults automatically allocate capital across Solana markets to optimize returns on stablecoins, SOL, and liquid staking tokens.

  • Project 0 claims targeted yields of more than 8% to 12% APY for stablecoins and over 15% for SOL-based assets.

  • The product uses unified margin to pursue lending-rate, carry, and basis opportunities across multiple protocols.

Project 0 has partnered with GammaFi to introduce machine-learning-powered vaults designed to automate yield strategies across Solana’s decentralized finance ecosystem.

According to an announcement published on Monday, the vaults use real-time machine learning to optimize returns on US dollar-denominated stablecoins, SOL and Solana liquid staking tokens.

Project 0 said the product targets annual percentage yields of more than 8% to 12% for stablecoin deposits and over 15% for SOL and liquid staking assets. These figures represent advertised targets rather than guaranteed returns and may change as market conditions evolve.

Machine Learning Automatically Allocates DeFi Capital

The new vaults use a machine-learning allocator to move capital between available opportunities based on factors such as interest rates, credit conditions and market risk.

This automated approach is intended to reduce the need for users to monitor individual lending platforms or manually rebalance positions as yields change.

Rather than relying on a human risk manager, the system evaluates market conditions and adjusts its allocation in real time. The objective is to identify the best risk-adjusted opportunities across several Solana-based DeFi markets.

Machine learning is becoming increasingly prominent in decentralized finance as protocols look for ways to manage complex strategies and rapidly changing market conditions. However, automated allocation also introduces model risk if the system misjudges volatility, liquidity, or the relationship between different assets.

Project 0 said the vaults differ from isolated-market products because they operate through a unified margin system.

Isolated vaults typically deploy capital within a single protocol or market. As more liquidity enters the same trade, interest-rate and basis opportunities can quickly become crowded, compressing yields and potentially exposing depositors to additional credit risk.

The Project 0 and GammaFi vaults instead seek opportunities across multiple markets. The system can pursue rate, carry, and basis trades over longer periods by combining positions from different Solana protocols.

One example provided by Project 0 involves lending assets through Jupiter or Kamino before borrowing against those positions natively through Project 0. This structure could allow the vaults to capture differences between lending and borrowing rates across platforms.

Project 0 Targets a DeFi Prime Brokerage Role

Project 0 describes itself as a programmable and generalized prime broker for decentralized finance.

In traditional markets, prime brokers provide institutions with services such as financing, margin management, and access to multiple trading venues. Project 0 aims to bring a similar model onchain by allowing positions held across different DeFi platforms to be managed under a unified framework.

The protocol said its infrastructure is audited and open source. Its integration with GammaFi combines that cross-market infrastructure with automated vault management.

While cross-market strategies can generate higher returns, they may also introduce more complex risks than simple lending products.

Users can face smart-contract vulnerabilities across several protocols, liquidation risk from borrowed positions, liquidity constraints, and losses caused by abrupt changes in interest rates. Performance may also depend on the machine-learning system responding correctly during periods of extreme volatility.

The advertised APYs are therefore not fixed. Actual returns will depend on borrowing demand, funding rates, asset prices, and the availability of profitable market spreads.

Despite those risks, the launch illustrates how Solana’s DeFi sector is expanding beyond basic lending and liquidity provision toward automated, cross-protocol asset management.

 

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Hassan Maishera

Hassan is a Nigeria-based financial content creator that has invested in many different blockchain projects, including Bitcoin, Ether, Stellar Lumens, Cardano, VeChain and Solana. He currently works as a financial markets and cryptocurrency writer and has contributed to a large number of the leading FX, stock and cryptocurrency blogs in the world.