Throughput Definition

What Is Throughput?

Throughput refers to the number of transactions or units of data a system can process within a given period. In the context of blockchains, throughput is most commonly measured by Transactions Per Second (TPS).

You can think of a blockchain as a multi-lane highway, where each car represents a transaction. The number of lanes and the speed limit together determine how many cars can pass in a second. The higher the throughput, the more “cars” pass through each second, resulting in less congestion. While TPS is the most widely used metric, other measurements like “data processed per second” or “transactions per block” are sometimes used depending on the scenario.

How Is Throughput Calculated in Blockchain?

Throughput is typically calculated as “the number of transactions included and confirmed in blocks over a time window, divided by the duration of that window.”

There are practical differences in calculation: some methods count when a transaction enters a block, while others use when it achieves finality. Finality refers to the state at which a transaction is considered irreversible on the network. Depending on which method is used, the throughput figure can differ slightly. For user experience, the focus is on how quickly transactions are added to a block; for security assessment, it's about how quickly they reach finality.

What’s the Difference Between Throughput, TPS, Latency, and Bandwidth?

Throughput and TPS are essentially synonymous in public blockchain discussions, but TPS is just a unit—throughput is a broader concept that can also refer to data or operations processed.

Latency measures the waiting time for a single transaction—similar to how long it takes a car to queue at and pass through a toll booth. Bandwidth is the maximum data capacity that can be transmitted per unit time, much like the number of cars that can travel down a highway per hour. High throughput does not always mean low latency—especially during periods of congestion, when queues increase latency. Even with sufficient bandwidth, conservative block parameters can still limit throughput.

Why Does Throughput Impact User Experience and Fees?

Higher throughput makes it easier for transactions to be included in blocks, which usually means shorter wait times and more predictable fees. When throughput is limited, the mempool (transaction waiting area) becomes congested. Users may raise their transaction fees to get priority processing, which leads to higher costs during peak times.

In decentralized applications (dApps), high-activity events like airdrops or popular NFT mints often lead to congestion. Insufficient throughput may cause failed or timed-out interactions. For cross-chain and payment scenarios, throughput directly impacts settlement speed for merchants and users.

What Technical Factors Affect Throughput?

Key factors influencing throughput include block time, block size and gas limits, consensus mechanism, and network propagation efficiency.

The gas limit defines the “computational budget” per block—gas can be viewed as the unit of cost for operations. The higher the gas limit per block, the more complex transactions can be included at once. Shorter block times mean more blocks per unit of time, increasing overall throughput. The consensus mechanism (e.g., proof-of-work or proof-of-stake) determines how quickly blocks are produced and synchronized across the network. Faster propagation means blocks are accepted by the whole network more quickly, reducing risks of rollbacks and conflicts.

How Is Throughput Improved with Layer 2 and Sharding?

Layer 2 solutions are secondary networks built on top of main blockchains; they process many transactions off-chain before submitting summaries or batches back to the main chain, thereby boosting overall throughput. Sharding partitions network state or data so that different nodes are responsible for different segments, reducing single-node load.

In recent years (2023–2025), Ethereum has significantly increased Layer 2 throughput via batching and data compression techniques. The rollout of EIP-4844 (also called “Proto-Danksharding”) in 2024 introduced cheaper data availability channels for Layer 2s, as highlighted in public materials from the community and foundation. Different approaches focus on different improvements: batching increases inclusion per time window, compression lowers data costs, and sharding enables parallelized processing.

How Is Throughput Measured?

Obtaining reliable throughput figures requires clear definitions and repeatable processes.

1. Define the measurement criteria: Are you counting “transactions included in blocks” or those reaching finality? Set a test duration (e.g., 5 minutes).
2. Generate sufficient load: Create a steady stream of transactions to avoid underestimating system performance.
3. Synchronize timing: Ensure all observation nodes have synchronized clocks and record precise start/end timestamps.
4. Collect and filter data: Retrieve transaction counts from blocks or node interfaces, filtering out failed or unbroadcast samples.
5. Calculate and verify: Divide total transactions by the time window for throughput; repeat tests at different times to observe congestion versus idle performance.
6. Document environment: Specify node hardware, network conditions, client versions, and parameters to ensure results are comparable.

How Does Throughput Affect Gate Use Cases?

When depositing or withdrawing on Gate, the throughput of your selected network impacts transaction speed and fees. High-throughput networks experience less congestion during peak times, usually resulting in faster confirmations. On congested or lower-throughput networks, transfers may take longer and require additional confirmations.

For example, during high-demand events on some mainnets, you may encounter queues. By choosing supported Layer 2 solutions (such as Rollups) on Gate for deposits, your transactions are likely to be processed faster on-chain. Conversely, choosing congested mainnets increases waiting times and potentially costs more in fees. When selecting a network, consider throughput, confirmation requirements, and fee structures to best balance speed and cost.

For fund security: Fewer confirmations do not guarantee finality—cross-chain transfers or large transactions should always wait for additional confirmations. Avoid using incompatible networks or address formats that may result in lost funds.

What Are the Risks and Trade-offs in Boosting Throughput?

Increasing throughput often involves trade-offs. Enlarging block size or reducing block intervals raises hardware and bandwidth requirements for nodes, possibly lowering decentralization by limiting node participation. Reducing safety buffers or accelerating confirmations can raise risks of chain reorganizations or rollbacks.

On Layer 2s, batching and compression improve throughput but introduce operational and bridging risks: unstable data channels or operator errors can impact batch submission and withdrawal timing. When choosing higher-throughput networks, weigh their degree of decentralization, finality mechanisms, and operational resilience.

How Should Throughput Be Evaluated Holistically?

Don’t rely on just one metric when evaluating throughput. Assess it alongside TPS, latency, fees, failure rates, and finality—including peak period performance. Users should choose networks that balance speed, cost, and reliability. On platforms like Gate, select deposit networks based on current congestion levels and confirmation requirements; reserve longer confirmation windows for large or critical transfers. Track scaling developments (such as Layer 2 data availability improvements and sharding) to make more informed decisions about future throughput trends and operational strategies.

FAQ

How does low throughput affect my transactions?

Low throughput directly causes transaction queues and network congestion. Your transaction may face long delays before being processed. During periods of high competition, you’ll need to pay higher fees for priority inclusion—significantly increasing your transaction costs. In severe cases, transactions may even timeout or fail.

Why does throughput vary on the same blockchain at different times?

Blockchain throughput fluctuates with network activity levels. When there’s a surge in user transactions (like popular ICOs or NFT launches), congestion increases—even if theoretical throughput stays constant—so actual processing capacity appears lower. Temporary factors like protocol upgrades or validator node status changes can also impact real-time throughput.

How can I determine if a blockchain’s throughput meets my needs?

Compare three key indicators: the chain’s advertised average TPS, its peak TPS, and your own business's required transaction frequency. For example, high-frequency trading may require TPS >1000, while simple transfers may only need tens of TPS. Also check recent congestion levels and average confirmation times—don’t rely solely on theoretical numbers. Gate provides real-time data on major chains for reference.

Why do some new public blockchains claim high throughput but still offer poor user experience?

Throughput is only one factor affecting user experience. Even with high TPS figures, slow final confirmation times, cross-chain bridge delays, or lack of on-chain applications can lead to poor usability. Some projects also exaggerate throughput claims; actual performance may drop sharply during congestion. Always verify with real-world tests rather than just whitepaper stats.

What is the relationship between throughput and transaction fees?

Lower throughput means more users competing for limited block space—requiring higher gas fees for priority processing. Conversely, high-throughput chains can handle more transactions with less competition for resources, so fees are typically lower. This is why users often prefer high-throughput Layer 2 networks (such as Arbitrum or Optimism); on Gate these networks usually offer significantly lower transaction costs compared to mainnets.