Why can't we just use one storage technology for everything?

Sun, 05 Jul 2026 22:41:00 +0000

Hello, I'm Ganesh. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free, unlimited, and source-available on Github. Star git-lrc on GitHub to help more developers discover the project. Do give it a try and share your feedback for improving the product.

Modern computers use multiple storage and memory technologies because no single medium can simultaneously provide massive capacity, ultra-low latency, high bandwidth, and low cost.

Every level of the memory hierarchy represents a tradeoff between these characteristics.

Why is there a memory hierarchy?

This is because each storage technology has its own advantages and disadvantages. We can't use one storage technology for all the purposes.

The main characteristics to consider are:

Capacity
Latency
Bandwidth
Cost

Each Storage technology is optimized for a specific purpose.

For example:
Imagine you have to travel from one place to another.
You can go with walking, bicycle, car, train, or plane.

Each mode of transport has its own advantages and disadvantages.

Another better example is think of using LLM model.

Depending on task complexity and size of data we can choose the model.

If we mess up with choosing model we will not get the desired output wheather cost will rise, or response will be very low quality.

Memory Hierarchy

Now I have listed down the memory hierarchy From top to bottom.

CPU Registers

This is the fastest storage available inside a processor.

Role:
Stores values currently being operated on by the CPU.

Characteristic	Value
Capacity	Extremely small (typically 32 × 64-bit registers per core)
Latency	~0.3 ns (about one CPU cycle)
Bandwidth	Highest in the system
Cost	Highest cost per bit (16 transistors per bit)

CPU Cache (L1, L2, L3)

A small, ultra-fast memory layer designed to keep frequently used data close to the processor.

Role: Reduces the need to access slower main memory.

Characteristic	Value
Capacity	Small (tens of MB total)
Latency	~7.5 ns (L3 cache)
Bandwidth	Extremely high
Cost	Very expensive (SRAM, 6 transistors per bit)

DRAM (Main Memory)

The working memory used by applications and operating systems.

Role: Holds active programs and data currently being used.

Characteristic	Value
Capacity	Typically 32 GB
Latency	~45 ns
Bandwidth	~48 GB/s
Cost	Higher than storage drives

GPU VRAM

Memory optimized for throughput rather than low latency.
Role: Feeds large amounts of data to thousands of GPU cores simultaneously.

Characteristic	Value
Capacity	~24 GB
Latency	~250 ns
Bandwidth	Extremely high
Cost	Expensive

NVMe SSD

High-speed solid-state storage connected through PCIe.
Role: Fast persistent storage for operating systems, applications, and files.

Characteristic	Value
Capacity	~2 TB
Read Latency	~80 μs
Write Latency	~500 μs
Bandwidth	~5 GB/s
Cost	~7 cents per GB

SATA SSD

An older solid-state storage technology using the SATA interface.

Role: Affordable solid-state storage with lower performance than NVMe.

Characteristic	Value
Capacity	Up to ~4 TB
Latency	~120 μs
Bandwidth	Lower than NVMe
Cost	Similar to NVMe SSDs

Hard Disk Drive (HDD)

Mechanical storage using spinning magnetic platters.
Role: Lowest-cost local storage for large datasets and archives.

Characteristic	Value
Capacity	~8 TB
Latency	~8.3–10 ms
Bandwidth	Very low compared to SSDs
Cost	~1–2 cents per GB

The Fundamental Tradeoff

As we move down the memory hierarchy:

Capacity increases.
Cost per GB decreases.
Latency increases.
Access speed decreases.
Distance from the processor increases.

By leveraging this trade-off, modern computers use a hierarchy of registers, cache, memory, SSDs, HDDs, and cloud storage instead of relying on a single storage technology.

Any feedback or contributors are welcome! It’s online, source-available, and ready for anyone to use.

⭐ Star git-lrc on GitHub

Credits

Here are the sources that inspired me to write an article on this topic.

Why Don’t Computers Just Use One Type of Memory?

Coder Legion - Recent posts tagged low