Skip to content

IOPS, Throughput, Bandwidth & Latency in Storage (Explained)

In the realm of storage technology, particularly Solid State Drives, three key metrics often dominate the conversation: IOPS (Input/Output Operations Per Second), Throughput, and Latency.

While these terms may seem like jargon to the uninitiated, they serve as critical indicators for storage performance.

IOPS gives us a measure of how many read and write operations a storage device can handle in a single second.

Throughput, often measured in megabytes per second, tells us about the actual data transfer rate.

Latency, on the other hand, reveals the time delay in transferring data blocks from one point to another.

Together, these metrics form the cornerstone of storage performance, influencing everything from application responsiveness to data transfer speeds. Also, these are crucial metrics to benchmark, determine, and describe the performance of servers, drives, and networks as well. We are going to talk about these in the context of storage and SSDs.

In this article, I’ll dissect each of these metrics, delve into their technical aspects, and explore their real-world implications.

Definition and Explanation

IOPS stands for Input/Output Operations Per Second, a metric used to quantify the performance of a storage device—be it an SSD, HDD, or any other type. Essentially, IOPS measures how many read and write operations a storage device can execute in one second. Each “operation” refers to a command to read from or write to a specific location on the storage medium. The higher the IOPS, the better the device can handle multiple read and write requests, which translates to better overall performance.

Importance in Storage Systems

IOPS is a critical factor in the performance of storage systems for several reasons. First, it directly impacts the speed at which data can be read from or written to the storage device, affecting everything from file transfers to how quickly applications respond to commands.

Second, in multi-user environments like data centers or enterprise storage solutions, high IOPS ensures that the system can handle multiple simultaneous requests without performance degradation. Lastly, IOPS is often a key consideration in workload balancing and resource allocation within complex storage architectures.

Factors Affecting IOPS

Several factors can influence the IOPS of a storage device:

  1. Type of NAND Flash: Thee type of NAND flash used in an SSD can have a significant impact on its IOPS. SLC NAND, which stores one bit per cell, generally offers higher IOPS compared to MLC or TLC NAND.
  2. Interface and Protocol: The interface and protocol used can also affect IOPS. NVMe SSDs generally offer higher IOPS than SATA SSDs due to the efficiency of the NVMe protocol and the high-speed PCIe interface.
  3. Firmware and Controller: The firmware and controller can be optimized to deliver higher IOPS for specific workloads. Some SSDs come with controllers that have built-in algorithms to manage the NAND cells more efficiently, thereby increasing IOPS.
  4. Data Block Size: The size of the data blocks being read or written can influence IOPS. Smaller block sizes usually result in higher IOPS because each operation is quicker to complete. However, this might not always be beneficial for workloads that require large data transfers.
  5. Queue Depth: A higher queue depth can lead to higher IOPS up to a certain point. However, if the queue depth is too high, it can cause the SSD to become bottlenecked, leading to a decrease in IOPS.
  6. Over-Provisioning: Over-provisioning, or allocating extra NAND capacity beyond what is usable, can improve IOPS by providing the SSD controller more room to manage data and execute read and write operations more efficiently.

IOPS in the context of SSDs

This metric is crucial for applications that require many small, random accesses, such as databases and virtual machines. SSDs generally offer higher IOPS compared to traditional Hard Disk Drives (HDDs) due to the absence of mechanical parts, enabling faster data access.

Table: IOPS Comparison Between Different Types of SSDs

Type of SSDAverage IOPS (Read)Average IOPS (Write)
SATA SSD100,00090,000
PCIe 3.0 SSD600,000550,000
PCIe 4.0 SSD1,000,000950,000
PCIe 5.0 SSD1,500,0001,400,000

Definition and Explanation

Throughput refers to the amount of data that can be transferred from one point to another within a given time frame, usually expressed in terms of bytes per second (B/s), kilobytes per second (KB/s), megabytes per second (MB/s), or even gigabytes per second (GB/s).

Unlike IOPS, which focuses on the number of operations per second, throughput is concerned with the volume of data moved. It’s a critical metric for understanding the performance capabilities of storage devices, network connections, and various other data transfer scenarios.

The Relationship Between IOPS and Throughput

While IOPS measures the number of individual read and write operations a storage device can handle per second, throughput measures the actual data volume transferred in these operations. The two are interrelated but not interchangeable.

High IOPS doesn’t necessarily mean high throughput, and vice versa. For example, a storage device might handle a large number of small read and write operations quickly (high IOPS) but still move a relatively small amount of data (low throughput). Conversely, a device might move large blocks of data less frequently, resulting in high throughput but lower IOPS.

Factors Affecting Throughput

Several factors can influence throughput:

  1. Type of Data Transfer: Sequential data transfers usually offer higher throughput than random transfers.
  2. Network Conditions: In the context of network storage, latency, and packet loss can significantly affect throughput.
  3. Hardware Limitations: The speed of the storage medium, as well as the read/write heads in HDDs, can limit throughput.
  4. Interface Type: The type of interface (SATA, NVMe, etc.) can also be a bottleneck for throughput.
  5. Software Overheads: File system type, encryption, and other software-level factors can also impact throughput.

Throughput in the Context of Solid-State Drives

When I talk about throughput in the context of Solid State Drives (SSDs), I’m referring to the amount of data that can be read from or written to the drive within a specific time frame. It’s usually measured in megabytes per second (MB/s) or gigabytes per second (GB/s).

Type of SSDAverage Throughput (Read)Average Throughput (Write)
SATA SSD550 MB/s520 MB/s
NVMe SSD3500 MB/s3300 MB/s
PCIe 4.0 SSD7000 MB/s6900 MB/s

Definition and Explanation

Latency refers to the time delay experienced in a system, specifically the time it takes for a specific block of data to travel from one point to another. In the context of storage systems and networks, latency is often measured in milliseconds (ms) and represents the delay between issuing a command and receiving a response. It’s a crucial metric for understanding the responsiveness of any system that involves data transfer or communication.

Factors Affecting Latency in Solid-State Drives

Several factors can contribute to latency in a system:

  1. Type of NAND Flash: The type of NAND flash used in the SSD can significantly affect latency. For example, SLC (Single-Level Cell) NAND generally offers lower latency compared to MLC (Multi-Level Cell) or TLC (Triple-Level Cell) NAND.
  2. Interface and Protocol: The interface and protocol used by the SSD also play a role in determining latency. NVMe (Non-Volatile Memory Express) SSDs connected via PCIe (Peripheral Component Interconnect Express) generally offer lower latency compared to SATA SSDs.
  3. Firmware and Controller: The firmware and controller architecture can also impact latency. Some SSDs are optimized for specific workloads, and their controllers are designed to minimize latency for those particular tasks.
  4. Queue Depth: Queue depth is the number of outstanding operations waiting to be processed by the SSD. A higher queue depth can sometimes result in increased latency due to the drive taking longer to process each individual operation.
  5. External Factors: External factors like the operating system, file system, and even other hardware components can also affect SSD latency. For example, older operating systems may not be optimized for SSDs and could introduce additional latency.

How Latency Impacts IOPS and Throughput

Latency has a direct impact on IOPS (Input/Output Operations Per Second) and throughput, two other key performance metrics. High latency can significantly reduce IOPS, as each operation takes longer to complete. This is especially true for random read or write operations, which are more sensitive to latency.

Latency in the context of Solid State Drives

Lower latency means faster data access, which is particularly important in time-sensitive applications like real-time analytics or high-frequency trading. SSDs generally have lower latency compared to HDDs because they don’t have moving parts, allowing for quicker data retrieval.

Table: Latency Comparison Between Different Types of SSDs

Type of SSDAverage Latency (Read)Average Latency (Write)
SATA SSD120 µs130 µs
NVMe SSD20 µs25 µs
PCIe 4.0 SSD10 µs15 µs

Definition and Explanation

Bandwidth is the data transfer capacity of a network or storage system, usually measured in bits per second (bps), megabits per second (Mbps), or gigabits per second (Gbps). In simpler terms, bandwidth is the “width” of the “pipe” through which data flows. The wider the pipe, the more data can flow through it simultaneously.

How Bandwidth Correlates with IOPS, Throughput, and Latency

  1. IOPS: Higher bandwidth generally allows for higher IOPS, as more data can be transferred in and out of the storage system or network. However, high bandwidth doesn’t automatically guarantee high IOPS, as other factors like the storage medium’s inherent speed and the system’s processing capabilities also play a role.
  2. Throughput: Bandwidth and throughput are closely related. Higher bandwidth usually means higher throughput because more data can flow through the system. However, throughput can also be affected by other factors like network congestion and data packet size.
  3. Latency: While it might seem intuitive to think that higher bandwidth would result in lower latency, this isn’t always the case. Latency is more about the speed of individual data packets traveling through the system, which can be affected by a variety of factors like distance, signal quality, and network traffic.

Why Bandwidth is Important in Certain Applications

  1. Data Backup and Recovery: Applications that require the transfer of large blocks of data benefit significantly from high bandwidth. The quicker data can be moved, the faster backup and recovery tasks can be completed.
  2. Streaming Services: High-definition video and audio streaming require high bandwidth to deliver content without buffering or quality loss.
  3. Cloud Computing: As more businesses move to the cloud, sufficient bandwidth is essential for quick data retrieval and real-time collaboration.
  4. Online Gaming: Multiplayer online games require high bandwidth to handle rapid, real-time data exchanges.
  5. Financial Trading: In financial markets, even a millisecond can make a difference. High bandwidth ensures that trading platforms can execute transactions with minimal delay.
  6. Scientific Research: Fields like genomics, climate modeling, and particle physics often involve the analysis of massive datasets, requiring high bandwidth for quick data transfer.

Imagine a highway system as a representation of a data storage or network system.

  1. IOPS (Input/Output Operations Per Second): Think of IOPS as the number of cars that can enter or exit the highway every second. A toll booth on the highway can serve as a good analogy. If the toll booth can handle 50 cars per minute, that’s your IOPS. If you have more toll booths (parallel processing), you can handle more cars (operations) per minute.
  2. Throughput: This is the total amount of data (or cars, in our analogy) that can pass through a given point in the system over a specific period. Imagine a checkpoint on the highway; the throughput would be the number of cars that pass through this checkpoint per hour.
  3. Latency: In our highway analogy, latency would be the time it takes for a car to travel from the entrance to the exit. This could be affected by various factors like speed limits, the number of lanes, or even obstacles like accidents or construction work.
  4. Bandwidth: Bandwidth in this analogy would be the number of lanes on the highway. A four-lane highway can carry more cars than a two-lane highway, assuming all other factors are equal. However, more lanes (higher bandwidth) don’t necessarily mean cars will reach their destination more quickly (low latency); it just means more cars can travel (higher throughput and potentially higher IOPS).

By using the highway analogy, you can see how all four elements are interrelated yet distinct:

  • More lanes (Bandwidth) allow for more cars (Throughput) and potentially faster entry/exit (higher IOPS), but not necessarily faster travel times (Latency).
  • More toll booths (higher IOPS) would mean cars can enter and exit faster, but it doesn’t mean the overall journey (Latency) will be quicker or that more cars can fit on the road (Bandwidth).
  • Faster speed limits (lower Latency) would mean quicker journeys, but it doesn’t automatically allow for more cars on the road (Bandwidth) or faster entry/exit (IOPS).
IOPSLow (50-150)High (3,000-50,000)Very High (up to 1,000,000)
Throughput80-160 MB/s200-550 MB/s5000-7000 MB/s
Latency5-10 ms0.1-0.5 ms0.01-0.1 ms
BandwidthLimitedHigherHighest (up to 64 Gb/s)

NVMe Gen 4 SSDs offer a significant performance boost in every metric compared to both HDDs and SATA SSDs. Their IOPS, throughput, and bandwidth are substantially higher, and they offer the lowest latency among the three. This makes them an excellent choice for applications requiring the highest levels of speed and responsiveness.

How These Metrics Affect Each Other

IOPS, Throughput, Bandwidth, and Latency are interconnected metrics that often influence each other. For example, a high IOPS rate usually implies that a storage system can handle a large number of small files quickly, but it doesn’t necessarily mean that the system will deliver high throughput. Throughput is also dependent on the size of each operation; hence, a system optimized for high IOPS may not deliver the best throughput. Similarly, both IOPS and throughput can be adversely affected by high latency. If each operation takes a long time to complete, then the number of operations per second (IOPS) and the amount of data transferred per second (Throughput) will naturally be lower.

What Happens When One is Optimized at the Expense of the Others

Optimizing one metric at the expense of others can lead to performance bottlenecks. For instance, if a system is optimized for high IOPS with small files, it may struggle with throughput when dealing with large files. Conversely, a system optimized for high throughput might not deliver the best performance when it comes to handling a large number of small files, thereby reducing its IOPS.

Real-world Applications and Crucial Metrics

ApplicationCrucial Metrics
Backup and RecoveryThroughput, Bandwidth
Video ProcessingThroughput, Bandwidth
DatabasesIOPS, Latency
TelecommunicationsIOPS, Latency

Backup and Recovery

In the realm of backup and recovery, throughput often takes center stage. The reason is simple: these operations often involve transferring large blocks of data. A high throughput ensures that these large data blocks are transferred quickly, reducing the time needed for backup and recovery operations. While IOPS and latency are still important, they are usually secondary considerations in this scenario.

Video Processing

Video processing is another domain where throughput is crucial. High-definition video files are large, and when editing or streaming them, the rate at which data can be read from or written to storage becomes a bottleneck. However, latency also plays a role here, especially in live streaming scenarios, where data needs to be transferred with minimal delay.


Databases are a bit more complex, as they often require a balance of all three metrics. High IOPS are crucial for databases that handle a large number of transactions per second, such as financial databases or high-traffic e-commerce sites. Throughput is important for databases that need to move large blocks of data for analytics or batch-processing tasks. Latency is always a concern in databases, as high latency can significantly slow down transaction times and query responses.


In telecommunications, especially in environments like call centers or real-time communication platforms, latency is the most critical metric. High latency can result in delays or dropped calls, severely affecting the quality of service. IOPS also matters here, particularly in systems that need to log a large number of small transactions quickly. Throughput is generally less of a concern in these environments, as the size of the data packets is usually small.

Hardware Considerations

  1. Storage Type: SSDs generally offer better IOPS and lower latency compared to traditional HDDs. For applications requiring high throughput, consider NVMe SSDs.
  2. Network Hardware: High-quality switches and routers can reduce network latency, thereby improving overall performance. For high-throughput needs, consider 10GbE or higher network interfaces.
  3. CPU and RAM: A faster CPU and more RAM can help in reducing latency and increasing IOPS, especially for data-intensive applications like databases.

Software Considerations

  1. File System: The choice of file system can impact all three metrics. For instance, ZFS and EXT4 are generally better at handling high IOPS workloads compared to older file systems like FAT32.
  2. Data Deduplication and Compression: These features can improve throughput but might increase CPU load, thereby affecting IOPS and latency.
  3. Caching: Implementing a robust caching strategy can significantly improve IOPS and reduce latency.

Best Practices

  1. Benchmarking: Always benchmark your storage systems using tools like Iometer for IOPS, iperf for throughput, and ping for latency to get a baseline performance metric.

Tools for Measuring Metrics

IOPSIometer, DiskSpd
Throughputiperf, NetStress
LatencyPing, Traceroute
  1. Load Balancing: Distributing workloads evenly across your storage resources can help in optimizing IOPS, throughput, and latency.
  2. Monitoring: Continuous monitoring can help you identify bottlenecks in real time, allowing you to take corrective actions before they impact performance.
  3. Updates and Patches: Keep your system up-to-date. Software updates often contain performance optimizations that can improve these metrics.
MetricFactors Affecting Metric
IOPSDisk speed, Queue depth
ThroughputDisk speed, Network speed, File size
LatencyDisk speed, Network congestion
BandwidthNetwork speed, Protocol

Thanks for Reading!

Notify of

Newest Most Voted
Inline Feedbacks
View all comments
Penn Chris
Penn Chris
1 month ago

Excellent & Well written article. Thank you for breaking it all down Barney style. For the Operating System Drive ( C: ) would it follow the same requirements as databases ? ( Since OS drives iften have to access many small system files, logs, .dll, apps, & programs, print queues, browser cache, drivers, and other system files all at once ? )