What Is the Solana Virtual Machine (SVM)?
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What Is the Solana Virtual Machine (SVM)?

What Is the Solana Virtual Machine (SVM)?

Yay─▒nlanma: May 1, 2024

Key Takeaways

  • The Solana Virtual Machine (SVM) is the underlying software infrastructure that enables the Solana blockchain to have higher transaction throughput and manage the execution of smart contracts.

  • Unlike the Ethereum Virtual Machine (EVM), which operates on a sequential processing model and uses Solidity, the SVM uses parallel transaction processing and the Rust programming language.

  • In this article, we will explore what the Solana Virtual Machine is, how it works, and some of its differences from the Ethereum Virtual Machine.


Originally, blockchains were primarily used as decentralized networks for processing transactions. However, virtual machines have enabled smart contracts to be built on top of blockchains, changing them into foundational layers for a wide variety of use cases and applications. The Ethereum Virtual Machine (EVM) and the Solana Virtual Machine (SVM) are prime examples. In this article, we will explore what the SVM is, how it works, and how it differs from the EVM.

What Is the Solana Virtual Machine (SVM)?

The SVM is the execution environment for smart contracts on the Solana blockchain. It can process thousands of transactions per second (TPS), improving the scalability of the network. 

Ethereum was the first to create a blockchain virtual machine, the EVM, which has since become the standard. EVMÔÇÖs architecture has inspired several blockchains, such as BNB Smart Chain, Avalanche, and Tron, which have developed systems forked or compatible with the EVM. The Solana Virtual Machine has emerged as a formidable competitor to the established EVM.┬á

How Does the Solana Virtual Machine Work?

The Solana Virtual Machine (SVM) is like a powerful computer that runs on the Solana blockchain and handles smart contracts created by users. We can break the SVM working mechanisms in a few different steps.

  1. Validator nodes. Solana has lots of validator nodes spread out globally. Each runs its own version of the SVM, meaning they can work on different tasks independently.

  2. Preparing smart contracts. To run a smart contract, the SVM first translates it into a language that the node can understand. This makes sure that the smart contract is executed correctly.

  3. Running the smart contracts. After the smart contract is in the right format, it gets executed. The smart contract updates some blockchain data on the particular nodeÔÇÖs version of the SVM that runs it.

  4. Reaching consensus. This updated version of the blockchain is shared with all the other network nodes to reach consensus.

LetÔÇÖs imagine that a user is using a decentralized application (DApp) built on Solana to buy and sell digital art. When they buy a piece of art, a smart contract is executed to update the ownership record on the blockchain. This smart contract is run through the SVM on one of the nodes, which checks the rules, makes sure the payment is legit, and updates the blockchain data.

Parallel Execution With SeaLevel

A distinct feature of the SVM is its ability to handle many smart contracts at the same time. That is achieved through parallel transaction processing. Essentially, the SVM executes multiple smart contracts in parallel, enhancing transaction throughput and efficiency.

SeaLevel is a component of the SVM that addresses the potential conflicts in parallel execution when multiple transactions affect the same account state at the same time. For instance, if two transactionsÔÇöone adding funds to a wallet and another withdrawing fundsÔÇöare executed simultaneously, it may lead to computational errors if not managed correctly.

SeaLevel is designed to manage dependencies between transactions explicitly. Smart contracts on Solana specify which parts of the blockchain's state each transaction will modify. This allows the system to identify transactions that can run independently (affecting different parts of the state) and those that are dependent (affecting the same part of the state). Dependent transactions are processed in a sequential order to prevent any conflict, ensuring that each transaction is executed accurately without compromising data and the blockchain's overall performance.


Transaction processing model

The SVM employs a parallel processing model, allowing multiple transactions to be executed simultaneously, which enhances throughput and reduces latency. Conversely, the EVM processes transactions sequentially, potentially leading to congestion during periods of high network use.

Programming language

The SVM supports Rust, a language known for its efficiency, particularly suitable for applications requiring high performance and security. Conversely, the EVM uses Solidity, a language designed specifically for smart contract development.

Smart contract deployment and execution

Smart contracts on the SVM are executed independently by each validator, enabling more efficient network operations. In contrast, the EVM requires that all nodes reach a consensus on the outcome of smart contract executions, which can slow down processing times.

Challenges of the SVM

The SVM faces various challenges. One of the main setbacks is the complexity of maintaining system stability and security in a parallel processing environment. While efficient, this architecture requires additional coordination to prevent conflicts and ensure integrity when transactions that affect the same data are processed simultaneously. 

In addition, the Rust programming language presents a steeper learning curve for new blockchain developers compared to Solidity and other programming languages used in blockchain development.

Closing Thoughts

The SVM is an execution environment on the Solana blockchain that emphasizes efficiency in transaction processing and smart contract execution. It uses parallel transaction processing and the Rust programming language to enable higher transaction throughput and better scalability. The SVM faces certain challenges, such as the steep learning curve for the Rust language and the inherent drawbacks of the parallel execution model. Still, the SVMÔÇÖs integration with emerging AI technologies promises an increase in its future usage and adoption.

Further Reading

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