Why GPU Memory Matters More Than You Think?

GPU memory importance matters because the data in it helps in complex mathematical, graphical and visual data operations.

Inadequacy of GPU memory resources may result in performance bottlenecks or unnecessary delays while the system shuffles small information packets from the CPU/global memory to GPU memory.

But there’s so much more to GPU memory than this, which we’ll cover in the article. Let’s dive right into the basics, shall we?

What is GPU Memory?

GPU memory is the on-chip memory available with Graphics Processing Units (GPUs) for storing transient data buffers.

GPU is a dedicated memory space separate from the system’s RAM. Like in all computation systems, GPUs’ on-chip memory plays a substantial role in storing and accessing data and processes for a short time.

A GPU device must often hold enormous data volumes within its memory space before instruction execution.

What is the Challenge with GPU Memory?

However, these intermittent storage requirements are often neglected when working with humongous, heavy-duty workloads such as Artificial Intelligence/Machine Learning models.

Memory usage and memory bandwidth are, in fact, the most overlooked aspects of GPU resource utilization. The choice between investing in on-premise GPUs and leveraging cloud GPUs is becoming increasingly crucial for businesses.

While owning and managing your hardware offers distinct advantages, the demand for cloud GPUs is surging, especially for applications in AI, machine learning and complex data analysis.

Why is GPU Memory Important?

Also known as Video Random Access Memory (VRAM), GPU memory enables the GPU to quickly reference massive datasets and process complicated resource-intensive tasks without overloading the system’s RAM and slowing the overall performance.

VRAM is widely acknowledged for its prowess in handling high-bandwidth data-intensive workloads such as 3D rendering, video playback, Graph Neural Networks (GNNs), gaming and blockchain calculations.

Not having enough bandwidth in VRAM for AI can cause debilitating performance issues in computing systems.

The global High-Bandwidth Memory (HBM) market is projected to reach approximately USD 12 billion by by 2031, growing at a CAGR of 33% in the next five years. This includes memory loaded onto GPU, CPU, APU, FPGA and ASIC devices.

Such tremendous market growth demonstrates the significant role that memory resources will continue to play in supporting computation workloads across industries.

Enterprises looking for GPU-accelerated Artificial Intelligence/Machine Learning, Image Processing, Deep Neural Networks or other resource-intensive operations must consider memory bandwidth a significant factor when selecting GPU resources for deployment.

On-chip memory must also be considered an important primary consideration, alongside GPU cores and dynamic partitioning possibilities.

What Are Different Types of GPU Memory?

Memory has always been a critical technology, enabling relentless advancements in various computation sectors.

Whether we talk about Big Data analytics, AI/ ML/ IoT-based industrial technologies or consumer-grade electronics like powerful smartphones, efficient memory utilization remains a dealbreaker across the spectrum.

There are different types of GPU memories:

• Register GPU Memory – Fast on-chip memory that stores operands used by GPU threads. This is the fastest GPU memory available and only accessible to the threads. It has the lifetime of a thread.
• Shared GPU Memory – This memory type is invoked when the GPU lacks VRAM availability. Multiple threads share these CUDA memory spaces within a GPU block while they handle resource-intensive tasks. Shared memory has the lifetime of the block in which it was created.
• Local GPU Memory – The OS kernel can also allocate GPU static memory. According to the CUDA programming approach, such memory is local to the operation and is only accessible by the thread to which it is assigned. It is significantly slow via-a-vis register or shared memory.

CPU Memory vs. GPU Memory: Key Differences and Importance

Central Processing Units (CPU) and Graphical Processing Units (GPU) both leverage memory resources to achieve their tasks and effectively fetch data for computation.

At the heart of every computer lies the CPU. A generalized processing unit handles the operating system and general tasks such as firewalls and web access. Thus, the memory it uses is also a generalized one (System RAM).

GPUs are specialized devices that handle complex, resource-intensive operations. As such, its numerous processing cores have access to dedicated VRAM to handle identical repetitive calculations.

Here is a list of differences between CPU memory and GPU memory:

CPU Memory	GPU Memory
System RAM, as the name suggests, specifically handles all the data associated with the system’s core operations.	Dedicated VRAM, as the name suggests, is meant for specialized purposes, such as video rendering, image-data processing and manipulation, and massive-scale dataset transmission for parallel processing.
The consumption of CPU memory is more compared to GPU memory. It is because the CPU handles OS tasks and related operations, including GPU management.	GPU handles task-specific operations only and therefore requires substantially less memory resources.
CPU memory consists of RAM, cache, and registers working in tandem. They have a short-width interface for data movement.	GPU memory refers specifically to on-chip storage resources. They have a broad interface & shorter paths with a point-to-point connection.
When a CPU works with its system memory, it focuses on delivering low latency.	When a GPU works with its dedicated memory, it focuses on delivering high throughput.
CPU memory bandwidth is slower compared to GPU memory bandwidth.	GPU memory bandwidth is faster compared to CPU memory bandwidth.

Also Read: How to Find Best GPU for Deep Learning

Importance of GPU Memory Bandwidth’s for Workloads

The memory bandwidth in GPU determines how fast it can transfer data to and from between processing cores and memory.

We can measure this by data transmission speed between memory and computation cores or via the number of links (in the form of buses) connecting these two parts.

Memory bandwidth in GPUs impacts various tasks, such as enhancing computational productivity, running healthcare applications or gaming.

Enhance productivity

Professional workloads and engineering tasks accomplished using tools like AutoCAD and Autodesk 3Ds Max demand robust systems to handle design processing and graphics-rich model development.

A GPU with more memory can hold a larger data cluster for processing. Furthermore, the better the memory bandwidth, the more swiftly it will process the stored data.

Streamlines gaming UX

Cloud servers that host online games must be backed by powerful GPUs that do not lag.

Apart from components like CPU and RAM, the importance of GPU memory and its bandwidth play a significant role in online games’ overall performance and display.

GPU bandwidth impacts gaming because the GPU resources directly manifest what you see on the screen.

Automotive industry

Automotive industries are developing driverless cars that source real-time images from multiple directions.

These driverless vehicles learn and adapt to a broad range of real-world scenarios.

To handle such unmatched image recognition potential, these systems require extensive processing capabilities backed by colossal memory resources and bandwidth to handle such multidirectional video data flows.

Healthcare and life science systems

For flawlessly generating and handling medical images from different healthcare equipment, medical systems require GPU resources with high memory bandwidth.

These systems can effortlessly crunch medical record data for better insights.

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Need for High Bandwidth Memory for Resource-intensive Tasks

We often encounter system bottlenecks despite having top-of-the-line advanced GPUs with thousands of cores. Why?

It is simply because dealing with resource-intensive tasks is not always about the number of dedicated processing cores available. Factors like memory bandwidth also play a substantial role.

Imagine a situation where your GPU has thousands of sophisticated CUDA and Tensor cores, but they are all stalled, waiting for the memory resources to free up from ongoing processes.

Having processing cores sitting idle is a waste of time and resources.

The memory bandwidth you must deploy depends entirely on the workload type and the computational resources it requires.

For example, if developing a large-scale ML project incorporating neural network layers, a wider memory bandwidth GPU will not let the project encounter bottlenecks.

The more memory bandwidth there is, the more efficiently the GPU cores undertake parallel processing.

Again, image and video-based ML projects such as image recognition and object identification demand more memory bandwidth than natural language processing (NLP) or sound processing workloads.

Enough memory bandwidth can accommodate a broad range of visual data in ML applications. Sound and text-based data are not heavy and can be handled on lower-memory GPUs.

For tailored solutions, explore our cloud GPU offerings designed to provide the high memory bandwidth necessary for resource-intensive tasks

Get the Best Cloud GPU Solutions with AceCloud

It’s clear that memory bandwidth is a critical factor in optimizing GPU performance for a wide range of resource-intensive tasks, from AI and machine learning to gaming and healthcare applications.

Ensuring that your systems are equipped with the appropriate memory bandwidth can mean the difference between smooth, efficient processing and costly bottlenecks.

AceCloud provides tailored GPU solutions aligned with your specific workload needs. Whether you’re running complex AI models, HD video processing, or compute-heavy tasks, our cloud GPUs deliver the high memory bandwidth required for peak performance. Call us at +91-789-789-0752 to speak with an expert today