Oasis
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Oasis in numbers
Oasis (ROSE) ranks #372 by market capitalization, at $67.37M. It belongs to the Others sector (Privacy). It has traded since Oct 28, 2020, across 35 markets on 19 exchanges, with $1.62M of volume in the last 24 hours. Its all-time high of $0.590217 was reached on Jan 15, 2022; the current price is 99.0% below that peak. Its cycle low of $0.00505542 came on Jul 28, 2026.
81.2% of the maximum supply of 10.00B ROSE is in circulation. 80.8% of the token allocation has been unlocked. The next scheduled unlock is on Nov 18, 2026. On BNB Chain, 2,209 addresses hold ROSE. It is available on 2 networks.
About Oasis
Oasis is a layer-1 blockchain built to support confidential applications at scale. It’s home to Sapphire, a private EVM network, Oasis Privacy Layer, a cross-chain privacy solution for EVM dApps, and Runtime Offchain Logic, a framework that makes off-chain computing trustworthy via onchain verification. Powered by the ROSE token, Oasis leverages a layered architecture and native rollup support.
Background
Debuting in 2018, Oasis set itself apart by splitting its consensus layer from its compute layer right from the outset. This division empowers Oasis with the adaptability to accommodate a variety of specific runtime environments, known as ParaTimes. The Oasis architecture is similar to the approach seen in Avalanche subnets or Polkadot’s parachains.
Oasis architecture separates the consensus layer, secured by a proof-of-stake mechanism that handles transaction ordering, validation, and global state, from the compute layer, where ParaTimes, like Sapphire, manage smart contract execution. Sapphire leverages Trusted Execution Environments (TEEs) to enable confidential smart contracts, encrypting data and computation within a secure hardware enclave that’s inaccessible even to network operators.
The recently launched Oasis Runtime OffChain Logic framework enables private, verifiable off-chain computation. It allows developers to run complex workloads (like AI training and inference) off-chain while maintaining privacy and trust through TEEs. This allows to support decentralized AI use cases such as private model training and inference, and lays the groundwork for trustless, verifiable AI.