The market has become completely desensitized to "high-speed public chains". Why is it said that Somnia may be different from the rest?

Author: TVBee

This article will analyze the following two questions:

Question 1: The market has completely desensitized to "high-speed public chains"; why is Somnia said to be potentially different?

Question 2: Is Somnia, which claims to be the fastest and most cost-effective parallel EVM Layer 1, just boasting?

➡️➡️➡️ Simple • Clean • Edition ⬅️⬅️⬅️

This section provides an overview of Somnia from three dimensions: technology, background, and ecosystem, allowing everyone to understand the highlights and advantages of the Somnia project.

💠Technical Highlights of Somnia

🔹Multi-Flow Consensus Algorithm: Data Chain + Consensus Chain, beneficial for preventing MEV, reducing redundancy, lowering costs while increasing efficiency.

🔹Innovative EVM Compiler: Achieves instruction-level parallel EVM, addressing high-frequency interactions in extreme cases.

🔹Self-developed IceDB database engine: improves data read and write speed and network stability.

🔹Data compression technology: Improve data transmission efficiency.

💠The background advantages of Somnia

🔹Team: The development team comes from Improbable, a multinational technology company founded in 2012, headquartered in London, UK. They have previously developed software, games, and Web3 metaverse products.

🔹Financing: A total of $270 million has been invested by well-known institutions such as MSquared, a16z, SoftBank, and Mirana.

💠Somnia's ecological progress

🔹Ecological Landscape: The Somnia testnet has already integrated 4 AI/social products, 7 games, 4 NFT projects, and 6 DeFi applications. Additionally, there are 2 AI/social products, 11 games, and 1 DeFi application set to launch soon.

🔹Ecosystem Data: Launched in late February 2025 until the time of writing this article (, on June 26, 2025 ), the Somnia testnet has produced over 100 million blocks, with an average block production time of 0.1 seconds. A total of 96,878,557 wallet addresses participated in the testnet, with a trading volume of 26.43 million transactions in the last day.

On the block explorer, you can often see the number of transactions and blocks flashing continuously, which Somnia refers to as "sub-second" and is visible to the naked eye.

💠Why is Somnia said to be different from others?

🔹High-frequency interaction: Although the market has become desensitized to the concept of "high-speed public chains", Somnia is not just pursuing technical metrics, but is focused on how to truly serve application scenarios with Web3 technology, especially in high-frequency related interaction areas such as gaming and socializing.

🔹Web3 and Web3 Integration: Somnia's unique background may play a key role in the integration of Web3 and Web2. Somnia has the potential to provide Web2 users with a seamless gateway into the Web3 world, which could lead to a truly user experience-centric application ecosystem.

➡️➡️➡️ Detailed • Explanation • Edition ⬅️⬅️⬅️

The previous section introduced the highlights, advantages, and ecological progress of Somnia. This part will provide an in-depth interpretation of Somnia's technology. It will explain how Somnia achieves high-frequency interactions technically, how it accomplishes low cost and high performance, and why Somnia is different from other parallel EVM projects.

💠Multi-Flow Consensus Algorithm: Data Chain + Consensus Chain

🔹Overview: Data Chain + Consensus Chain Structure

Somnia adopts a new multi-stream consensus (MULTISTREAM) algorithm.

The so-called multi-chain refers to Somnia recording transaction information across multiple data chains, with each data chain being recorded by one validator, and each validator unable to interfere with the data chains of others.

Consensus refers to the execution of consensus on the Somnia consensus chain, which sorts transactions and records references to transactions on the consensus chain. The consensus chain is jointly executed and maintained by all validators.

🔹Overview: The workflow of Somnia's multi-stream consensus

After a user sends a request to the Somnia network, the validator who receives the request will write the transaction into the data chain.

b The consensus chain uploads and downloads the top data shards of the data chain with other data chain validators every time period (, such as 30 seconds, 1 second, etc. ).

Validator C will write a complete data shard, which includes a collection of all data sharding at the top of the data chain, into the consensus chain.

The validators sort the transactions, update the status based on the sorted transactions, and all validators synchronously write to Somnia's IceDB database.

🔹Highlights: Somnia's transaction ordering is beneficial for preventing MEV.

Somnia uses a deterministic pseudorandom function to sort transactions.

We know that there is no true randomness in computing programs; rather, it is achieved through algorithms that create pseudo-randomness. Deterministic pseudo-random functions have two characteristics: one is randomness, meaning that the next generated random number cannot be predicted, but each validator will generate the same random number in a fixed order during execution.

In this way, all validators run the same deterministic pseudo-random function, generating a series of identical random numbers, which are used to sort the data chain. Based on this, transactions for this period are sorted.

For example, the sorted data chain is B, A, C...

So the transaction order is that the transactions from data chain B come first, followed by data chain A, data chain C, and so on. Of course, this process will remove duplicate transactions based on the hash values.

Of course, the ordering of the data chain is fixed, but the transaction order in different data chains may vary. For example, in data chain A, it might be transaction 1 first and transaction 2 second, while in data chain B, it might be transaction 2 first and transaction 1 second. Since the ordering of the data chains is B before A, the final transaction order is transaction 2 first and transaction 1 second.

The advantage of this sorting method is that it is difficult for MEV attackers to bribe validators, as they do not know how the corresponding data chain of the validators will be sorted. If there are a total of 100 validator nodes in the network, even if the MEV attacker bribes 50 validators, as long as there is one validator that has not been bribed, (, and the attacked transaction, ), is ranked ahead of these 50 validators, the consensus chain will record transactions in the correct order, and the MEV attack will fail.

🔹Highlights: Reduce redundancy, lower costs and increase efficiency

On one hand, each validator in Somnia records a separate data chain without a data validation process among validators. When transmitting snapshots, only the snapshot information of each data chain is transmitted, and the snapshot information does not include specific transaction details, thus reducing interaction redundancy.

On the other hand, the various data chains of Somnia do not need to synchronize information from other data chains, and the consensus chain does not record transaction information. Instead, it records snapshots of data chain information and sorted transaction references' hash values ( every time period. This reduces storage redundancy.

Somnia can work more efficiently due to the reduction of redundant interactions.

Due to reduced storage redundancy, Somnia requires lower costs during operation.

🔹Supplement: The data chain's tamper-proofing

Although there is no information verification on the data chain, validators cannot tamper with transaction information. This is because if a validator alters the transaction information, it will affect the hash value of the transaction and the hash values of subsequent transactions, leading to a conflict between its information and the information stored on the consensus chain.

💠Instruction-level parallel EVM

🔹Pain Point: It's difficult to improve the congestion of high-frequency interactions in parallel trading.

Somnia's parallel EVM is different from Monad and Reddio; the EVM parallelism of these three chains is transaction parallelism, which means that transactions are processed in parallel to enhance transaction speed.

Among them, Monad allows optimistic parallel transactions and corrects conflicts when detected. Reddio performs parallel transactions that are non-conflicting and have no dependencies.

However, when a large number of related transactions occur, the transactions cannot be executed in parallel, making congestion likely. There are two extreme examples, such as a sudden influx of users on the network trading a certain token with USDC, where these transactions cannot be executed in parallel due to the need to trade with the LP pool and can only be executed sequentially.

Another extreme example is that countless people rush to mint the same NFT, which also cannot be done in parallel because the number of NFTs is limited. It must be executed in sequence to determine which people can successfully mint and which others fail.

Reddio's solution to this problem is to use GPUs, leveraging the powerful computing capabilities of GPUs to address the congestion caused by high-frequency interactions. While this can improve transaction efficiency, it also increases the cost of transactions.

🔹Highlights: Instruction-level parallel EVM

In order to solve the congestion problem caused by a large number of related transactions occurring simultaneously and the difficulty of resolving transaction parallelism, Somnia has innovatively developed an EVM compiler.

In the standard EVM execution process, instructions in a transaction can only be executed sequentially and one by one. However, Somnia supports splitting transactions into several instruction sets, which can be executed in parallel if they are non-conflicting and have no dependencies.

Taking Swap trading as an example, it can be divided into several instruction sets based on functionality: parameter validation, parameter processing, balance checking, authorization checking, pool status checking, price calculation, fee calculation, transferring input tokens, updating pool status and fee records, transferring output tokens, and event emission. Among them, instruction sets that are non-conflicting and have no dependencies can be executed in parallel, thereby improving the execution efficiency of transactions.

The key to the parallel EVM of the instruction set is Somnia's unique EVM compiler, which compiles EVM bytecode into x86 machine code. Modern CPUs are multi-threaded cores, and each CPU core can execute machine code in parallel on multiple threads, allowing several instruction sets of the EVM to be processed in parallel, thus increasing the execution speed of individual transactions. Therefore, Somnia can also be referred to as a hardware-level parallel EVM.

🔹Highlights: Dual advantages of cost and efficiency

Standard EVM's interpreted execution: Transaction 1 → parsed into bytecode → sequential interpreted execution → Transaction 2 → parsed into bytecode → sequential interpreted execution → Transaction 3 → parsed into bytecode → sequential interpreted execution...

Somnia's EVM compilation execution: Contract code → Parsed into bytecode → Dynamically compiled into machine code → Parallel execution of transaction 1's instruction set → Parallel execution of transaction 2's instruction set → Parallel execution of transaction 3's instruction set…

The comparison shows that the more transactions, the more advantageous Somnia's EVM compilation execution is.

Therefore, for ordinary non-high-frequency trading, Somnia still uses standard EVM interpretation execution, parsing the smart contract code into EVM bytecode each time the EVM is executed, interpreting and executing it in order.

For centralized high-frequency trading, Somnia enables the EVM compiler to compile EVM bytecode into x86 machine code. The machine code can then be executed repeatedly according to parameters to quickly complete centralized high-frequency trades, achieving results that parallel EVMs at the transaction level cannot reach.

Therefore, Somnia can achieve a dual advantage in cost and efficiency.

💠IceDB database engine

🔹Overview: Using LSM trees to replace the Merkle tree data structure

The vast majority of blockchains use the data structure of Merkle Trees. The leaf nodes of a Merkle Tree store the hash values of transaction data (or the transaction data itself, which is then hashed), while the non-leaf nodes store the hash of the hash values of their child nodes, calculating hash values in pairs layer by layer until a Merkle Root is computed, thereby safely verifying the integrity of the data within the block and preventing data tampering.

Taking the data storage Merkle tree of ERC20 token contracts as an example, the leaf nodes of the Merkle tree include:

• Total supply of tokens )TotalSupply(, token symbol )NameSymbol( and other attributes, each attribute corresponds to a key )attribute name ( and a value )attribute value (;

• The holding status of all token holding addresses, with each address corresponding to a key ) address hash ( and a value ) holding quantity (;

• The authorization status of the token, each authorized address corresponds to a key ), an address hash (, and a value ) representing the authorized amount (;

……

If an ERC token has 4 attributes, 32,000 holding addresses, and 2,764 authorized addresses. This number is obviously not large. However, there are a total of 32,768 leaf nodes, and the Merkle rights written to this token require calculating 65,535 hashes.

The IceDB database engine developed by Somnia does not use the commonly used Merkle tree data structure, so there is no hash root in its block information.

IceDB uses LSM tree ) Log-Structured Merge-Tree, which is a log-structured tree data structure. Its main feature is that data is written in an append-only manner rather than being modified in place, thus eliminating the issue of tampering.

The writing to the IceDB database first goes into the memory's MemTable. When the MemTable is full, it is flushed to disk, forming an SSTable. LSM periodically merges SSTables while deleting duplicate keys.

This process does not require hash calculation; it only needs to write new data to the MemTable. Therefore, whether the data is written to memory, cache, or disk, the write speed of the IceDB database is significantly faster.

🔹Highlights: Faster read and write speeds

The LSM tree data structure clearly has performance advantages in terms of writing data. In addition, the Somnia technical documentation mentions that "a data cache has been created that can optimize both reading and writing simultaneously, making the average read and write time of IceDB between 15 and 100 nanoseconds."

🔹Features: Read and write performance reports with fair and effective Gas

In most blockchain networks, although the final validator nodes tend to store the same data, there can be discrepancies between the data stored in the memory and disk of different validator nodes in the short term. This results in users consuming different amounts of Gas when reading and writing data due to accessing different locations. On the other hand, because of differing access locations, the time taken by users to read and write data may be prolonged, and during this time window, the network Gas may fluctuate. Therefore, it is challenging to determine a fair and effective Gas. If Gas is underestimated, nodes might become passive due to low earnings, affecting network efficiency. If Gas is overestimated, users may incur unnecessary additional costs and potentially provide opportunities for MEV attacks.

Under the IceDB database engine, users do not find the required data in the cache each time they read and write data. Therefore, it is necessary to read data from both memory and SSD, calculate the frequency of data read from memory and SSD, and return a "performance report". The "performance report" provides a deterministic basis for calculating the Gas required by users, making network Gas fairer and more efficient, which is beneficial for network stablecoins.

💠Data Compression Technology

According to the information and frequency distribution power law theory introduced in the Somnia technical documentation, summarizing based on the probability of information occurrence can achieve a high compression ratio for the data.

Each data chain in Somnia is managed by a validator, who does not need to send the entire block but only needs to send the information stream. Stream compression has a higher compression rate, which is beneficial for improving network transmission capacity.

In addition, Somnia uses BLS signatures to improve the speed of signature transmission and verification.

Under the multi-flow consensus algorithm of Somnia, the validator nodes of the data chain send data shards to each other without a centralized leader for centralized data upload and download. Bandwidth can be evenly distributed among the validators. Each validator must send data shards to other validators while also downloading data shards sent by other validators, so the bandwidth required for each validator's uploads and downloads is symmetrical. Therefore, the transmission capacity of the Somnia network will be relatively balanced and stable.

💠Written at the end

Although Web3 appears to be more advanced than Web2 on the surface, the technology framework of Web2 is often more complex and mature. When Web2 developers participate in Web3 development, their technical background can bring more innovation to the blockchain world.