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GPU → vs APU
APU vs GPU and which is best for your needs
APU and GPU are two types of sophisticated processing units. Both augment a machine’s general CPU and improve efficiency, but their purposes, uses, and key strengths are distinct. Learn more about each and how they compare, so you can decide which is best for your organization.
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What is an APU?
An Accelerated Processing Unit (APU) is a type of microprocessor that combines a central processing unit (CPU) and a graphics processing unit (GPU) on a single chip.
APUs were developed by AMD to improve power efficiency and cost-effectiveness, compared to housing a separate CPU and GPU in the same machine. They enable enhanced graphics performance without requiring a dedicated graphics card, making them ideal for budget gaming, multimedia applications, and compact devices like laptops and mini PCs.
It’s worth noting that Intel has a similar alternative called an Integrated GPU/CPU (iGPU) that is used by other manufacturers (Intel, mostly).
What is a GPU?
A Graphics Processing Unit (GPU) is a specialized processor designed to handle complex computations, particularly those involving parallel processing.
Originally developed to accelerate rendering in video games and graphics applications, modern GPUs have evolved into powerful computing engines used for a wide range of tasks beyond graphics. Today, GPUs are essential for artificial intelligence (AI) and machine learning, scientific simulations, cryptocurrency mining, medical imaging, high-performance computing, and more.
GPUs’ ability to simultaneously process thousands of operations makes them ideal for workloads that require massive data parallelism, significantly improving efficiency in fields like deep learning, data analysis, and even cloud computing.
APU benefits
An APU offers several advantages, particularly for users seeking a balance between performance, power efficiency, and cost-effectiveness. Some of the key benefits include:
- Cost savings: Since an APU eliminates the need for a separate GPU, it lowers hardware costs, making it ideal for budget-friendly systems.
- Power efficiency: APUs consume less power than systems with both a standalone CPU and GPU, leading to lower energy usage and heat output.
- Compact design: The integration of CPU and GPU onto a single chip enables smaller system builds, ideal for compact desktops, laptops, and embedded devices.
APUs do offer good graphics performance for casual media use and general computing, though they are not as powerful as discrete GPUs.
GPU benefits
Using a CPU with a separate GPU rather than an APU offers several advantages as well, particularly for high-performance computing, gaming, and professional workloads.
- Superior performance: A dedicated GPU has significantly more processing power and memory bandwidth than an APU’s integrated graphics. High-performance, multi-core graphics processors that are much more powerful and suited for parallel computing tasks like AI training, machine learning, and big data analysis.
- Scalability: Users can upgrade their GPU separately from the CPU, allowing them to keep up with technological advancements without replacing the entire system.
- Higher VRAM capacity: Discrete GPUs come with their own dedicated video memory (VRAM), reducing the burden on system RAM and improving performance in graphics-intensive applications.
- Better heat management: Since the CPU and GPU are separate, heat is distributed more efficiently, allowing for better cooling and overclocking potential.
- Dedicated compute and parallel processing: GPU servers allow CPUs to focus on computational tasks while GPUs handle data-heavy workloads, improving overall efficiency.
- Optimized for virtualization and cloud computing: High-performance GPUs in servers support advanced virtualization technologies, making them ideal for cloud gaming, VDI (Virtual Desktop Infrastructure), and AI-driven cloud services.
APU machines are cost-efficient and power-friendly. GPUs dominate in high-performance applications, offering better scalability, computational power, and specialization.
APU vs GPU use cases
APUs excel in situations where a balance of CPU and GPU performance is needed without the cost, power, or space requirements of a dedicated GPU. Ideal use cases include:
- Budget gaming PCs: APUs with integrated graphics offer decent gaming performance for casual or esports titles without requiring a dedicated GPU.
- General office and productivity workloads: APUs provide sufficient power for office applications, web browsing, video streaming, and light creative work like photo editing.
- Education and entry-level computing: Affordable laptops and desktop systems for students and educational institutions often rely on APUs to provide adequate performance for basic tasks.
- Edge computing and IoT applications: APUs are well-suited for embedded computing, industrial automation, and edge AI, where modest graphical capabilities and power efficiency are needed.
GPUs shine in scenarios that demand high-performance graphical or computational power, including:
- High-end gaming and VR: AAA gaming, virtual reality (VR), and high-resolution gaming (1440p/4K) require dedicated GPUs for smooth performance and advanced graphical effects.
- AI, machine learning, and deep learning: GPUs, particularly NVIDIA’s CUDA-enabled and AMD ROCm-supported models, accelerate AI workloads, neural networks, and data science applications.
- Scientific computing and big data analysis: GPUs handle massively parallel computations used in physics simulations, climate modeling, and computational biology.
- High-performance computing (HPC): Supercomputers and enterprise AI servers leverage multi-GPU setups to process complex datasets at high speeds.
Choosing between an APU vs a GPU
- If you need a cost-effective, power-efficient solution for everyday computing, light gaming, or embedded applications, an APU is a great choice.
- If you require high-performance computing, gaming, AI processing, or professional graphics workloads, a dedicated GPU is essential.
For businesses and servers, GPU-based infrastructure is crucial for demanding workloads, while APU-based servers may be sufficient for lightweight tasks, virtualization, and edge computing.
Getting started with GPUs
If you decide that your organization needs the raw compute power of a dedicated GPU server, the next step is to make sure you partner with a top-tier bare metal or dedicated hosting provider.
Look for a hosting provider that has been around longer than GPU server hosting. A provider with a good track record of dedicated server hosting knows what customers need. Then, check their GPU hardware.
Liquid Web’s GPU server hosting stands out for high-performance hardware (featuring advanced NVIDIA GPU like the L4 and H100 NVL), paired with powerful AMD EPYC CPU, and high-speed DDR5 memory for handling demanding workloads such as AI and machine learning.
Click below to explore GPU server options or start a chat to talk to one of our experts about GPU hosting!
APU vs GPU FAQ
What’s the difference between an APU and an integrated graphics card?
While APUs are a specific type of processor optimized for better graphics performance, standard integrated graphics generally offer lower performance and are meant for everyday computing rather than gaming or media-heavy applications.
An APU is a processor developed by AMD that combines a CPU and GPU on a single chip, offering better graphics performance than standard integrated graphics. APUs are designed to handle light gaming, media streaming, and productivity tasks without the need for a dedicated graphics card. They share system memory (RAM) instead of having dedicated VRAM but provide more powerful graphical capabilities compared to basic integrated graphics solutions.
By contrast, integrated graphics refers to any GPU built into a CPU, found in both Intel and AMD processors. These GPUs are primarily designed for basic tasks like web browsing, office work, and video playback, rather than gaming or intensive workloads.
What is a CPU?
A Central Processing Unit (CPU) is the primary component of a computer that executes instructions and performs calculations necessary for running software and operating systems. Often referred to as the “brain” of the computer, the CPU processes data, manages tasks, and coordinates system operations by executing billions of instructions per second.
Additional resources
What is a GPU? →
What is, how it works, common use cases, and more
CPU vs GPU →
Similarities, differences, benefits, challenges, and how to decide what you need
What is GPU as a Service? →
Learn what it is and what it isn’t, how it compares to cloud GPU and bare metal GPU, and more
Kelly Goolsby has worked in the hosting industry for nearly 16 years and loves seeing clients use new technologies to build businesses and solve problems. Kelly loves having a hand in developing new products and helping clients learn how to use them.