GPU Buying Guide: How To Choose the Right GPU in 2025

With the sheer range of graphics cards available today, deciding on the right GPU may seem daunting. But don't worry, choosing the appropriate graphics processing unit doesn't need to be confusing if you understand the basics of a GPU, the types of GPUs designed for common usage scenarios and the key specifications that impact performance for high-performance computing like Gaming, Artificial Intelligence, Machine Learning, Deep Learning, Data Analytics and Rendering. This comprehensive GPU guide will cover everything you need to know about choosing the right GPU for various workloads. 

Understanding GPU Basics

Before moving on to specific use cases and recommendations, you must understand what a GPU is and the underlying architecture that gives it its performance capabilities.

GPU and its Role in Computing

A GPU, or graphics processing unit, is a specialised circuit designed to rapidly manipulate and alter memory to accelerate the building of images intended for output to a display. Unlike a CPU which has just a few cores optimised for sequential serial processing, a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks concurrently. Having lots of simple cores allows GPUs to excel at the types of repetitious mathematical operations involved in rendering graphics far quicker than a general-purpose CPU ever could. From generating complex 3D scenes and textures for video games, all the way to accelerating machine learning algorithms, neural networks and scientific simulations, today’s GPU applications extend far beyond just real-time graphics.

NVIDIA is one of the most popular GPU manufacturers that provide the graphics cards and chipsets found in everything from consumer video game consoles and mobile devices, all the way up to supercomputers. With the rising computational demands of modern software and the surge of growth in AI services, there has been an exponential increase in demand and innovation in the GPU market. High-performance computing tasks such as training deep neural networks require immense computational power of data centre-grade GPUs. These data centre GPUs are designed to be integrated into supercomputing clusters or cloud-based infrastructure to tackle massive parallel processing capabilities necessary for cutting-edge applications in fields like artificial intelligence, scientific simulations and big data analysis.

However, owning and operating such high-end data centre GPU resources can be a significant company investment. Building and maintaining an on-premise supercomputing facility requires not only the acquisition of the GPU hardware itself but also the associated infrastructure, power and cooling systems and a dedicated team of experts to maintain the complex environment. Fortunately, companies can opt for cloud-based solutions where they can rent GPU computing resources.

Our cloud platform provides on-demand access to varying configurations of remote virtual machines equipped with specialised GPU hardware allowing you to simply rent the processing power as needed. You can provision GPU compute resources starting from as little as $ 0.43 per hour. Our pay-as-you-go cloud GPU pricing alleviates resource bottlenecks and democratises access so anyone can leverage cutting-edge GPU innovations without massive investment. So, you no longer need to invest in costly desktop workstations or maintain on-premise Data Centre infrastructure to leverage these teraflop processing capabilities. 

GPU Architecture 

Now that you know what a GPU is, let’s understand the architecture in depth to make the best and most informed choice when selecting a graphics card for your needs:

• CUDA Cores: CUDA (Compute Unified Device Architecture) cores are the primary units of computation in NVIDIA GPUs. They are designed to handle a wide range of computational tasks efficiently. Each CUDA core can execute a floating-point or integer operation per clock cycle, making them highly effective for parallel processing tasks.

• Tensor Cores: Introduced with the NVIDIA Volta architecture, Tensor Cores are specialised processing units designed to accelerate deep learning tasks. They perform matrix operations that are common in neural network training and inference, offering significant speedups in AI applications. Tensor Cores can perform mixed-precision arithmetic, using lower precision for higher throughput while maintaining accuracy.

• Ray Tracing Cores (RT Cores): Starting with the Turing architecture, NVIDIA introduced RT Cores to accelerate ray tracing calculations. Ray tracing simulates the way light interacts with objects in a virtual environment to produce highly realistic lighting effects. RT Cores accelerate the bounding box intersection tests and ray-triangle intersection tests, essential operations in ray tracing.

• NVLink: NVLink is a high-bandwidth interconnect developed by NVIDIA to allow data to be quickly transferred between GPUs and between GPUs and CPUs. This is particularly important in multi-GPU configurations, enabling faster data exchange and improving overall system performance in compute-intensive applications.

• SM (Streaming Multiprocessor): The GPU is divided into multiple SMs, each containing several CUDA Cores, Tensor Cores, RT Cores, caches, and other resources. The SM is the fundamental building block of NVIDIA GPU architecture, designed for parallel processing of multiple threads.

• Mixed-Precision Computing: Modern NVIDIA GPUs, through their Tensor Cores, support mixed-precision computing, allowing calculations to be performed in FP32, FP16, or even INT8 precision. This flexibility enables optimisations for speed and memory usage without significantly impacting the accuracy of computations, especially in AI and deep learning applications.

• Memory Architecture: Modern GPUs feature a hierarchical memory architecture to efficiently manage data access and storage. This includes

  • GDDR Memory (Graphics Double Data Rate): A type of high-speed memory used in GPUs to store textures, frame buffers, and other graphics-related data. GDDR6 and GDDR6X are the latest standards, offering higher bandwidth and lower power consumption.
  • HBM (High Bandwidth Memory): HBM stacks multiple memory dies on top of one another, connected through silicon vias (TSVs). This design significantly increases memory bandwidth while reducing power consumption and physical space requirements.

Assessing Your Needs: Guide to GPUs

Once you've grasped the architecture of a GPU, it's essential to determine how each feature contributes to optimising your workloads. Moving forward, you will understand how to pick a GPU for your specific workloads:

Rendering

If you're working on rendering tasks, then consider leveraging CUDA Cores for efficient processing of complex calculations. For enhanced performance and realistic lighting effects, take advantage of Ray Tracing Cores (RT Cores) to accelerate ray tracing calculations. To ensure the smooth handling of large textures and frame buffers, opt for memory architectures like high-speed GDDR or HBM.

Machine Learning

When tackling machine learning tasks, ensure that you use the power of CUDA Cores for parallel computations necessary for both training and inference. For accelerated deep learning operations, utilise Tensor Cores to significantly improve the speed and efficiency of ML algorithms. In multi-GPU configurations, leverage NVLink for faster data exchange and optimised performance in training large-scale ML models.

Deep Learning

For deep learning tasks, you can utilise Tensor Cores to enable faster training of complex neural networks and inference tasks. Take advantage of mixed-precision computing supported by Tensor Cores for optimised speed and memory usage without compromising accuracy.

Data Analytics

In data analytics tasks, don’t forget to make use of CUDA Cores for efficient processing of vast amounts of data. Optimise data storage and retrieval with high-speed memory architectures such as GDDR and HBM to minimise latency. Utilise NVLink for seamless data transfer between GPUs, enhancing parallel processing capabilities in data analytics workflows.

Performance and Specifications

Now that you've determined the requirements of your workloads, you can learn how various specifications will influence GPU benchmarks.

• Clock Speed: Higher clock speeds generally result in faster processing of instructions and calculations. Increased clock speed leads to better performance in tasks that are heavily dependent on single-threaded processing. Overclocking can further enhance performance but may also lead to increased heat generation and power consumption.

• Memory: Adequate memory capacity (VRAM) is crucial for storing and accessing large amounts of data, textures, and intermediate computations. Insufficient memory can lead to performance bottlenecks, especially in applications that require working with large datasets or high-resolution textures. Faster memory speeds (measured in memory clock frequency) can improve data transfer rates and overall GPU performance.

• Bandwidth: Memory bandwidth determines how quickly data can be transferred between the GPU's memory and the processing units. Higher memory bandwidth allows for faster data exchange, which is particularly beneficial for memory-intensive tasks such as high-resolution gaming or complex simulations. Optimal memory bandwidth ensures that the GPU can efficiently handle data-intensive workloads without being constrained by memory access speeds.

• Power Consumption: Efficient power consumption is essential for maintaining performance while minimising energy usage and heat dissipation. GPUs with efficient power management features can dynamically adjust power consumption based on workload, further optimising performance per watt. 

ESG Impact and Considerations

Balancing performance with power consumption is important as excessively power-hungry GPUs demand substantial electrical power to operate at their full potential, often resulting in increased heat generation and energy consumption. So, this energy-intensive nature of GPUs raises concerns about their environmental impact.

ESG Concerns

The primary ESG (Environmental, Social, and Governance) concern associated with the use of GPUs is their significant energy consumption. 

• Environmental Impact: Training complex AI models or running extensive simulations requires substantial computational resources, leading to high electricity usage and, consequently, a larger carbon footprint. This is especially concerning when considering the global push towards reducing greenhouse gas emissions to combat climate change.

• Social and Governance Impact: On the social and governance fronts, the high energy demands of GPUs raise questions about equitable access to computational resources, as only organisations with significant financial resources can afford the operational costs. The reliance on non-renewable energy sources for powering these computations can exacerbate environmental inequalities and contribute to governance challenges related to energy policy and sustainability commitments.

Hyperstack's Green Commitment

Hyperstack, recognising the critical need to address these ESG concerns, has positioned itself as a sustainable solution by ensuring that all its GPU infrastructure is powered by 100% renewable energy sources. This approach significantly mitigates the environmental impact of using power-hungry GPUs in several ways:

• Reducing Carbon Footprint: By sourcing energy from renewable sources such as hydropower, Hyperstack eliminates the carbon emissions associated with traditional energy production. This directly contributes to the global efforts to combat climate change and aligns with the sustainability goals of many organisations seeking to minimise their environmental impact.

• Supporting Sustainable Governance: By committing to renewable energy, Hyperstack sets a precedent for responsible energy consumption in the tech industry, influencing governance policies and practices. This commitment can encourage regulatory bodies and other organisations to prioritise sustainability in their operations and policies, furthering the governance aspect of ESG considerations.

Budget Considerations

When looking for the right GPU, it's imperative to prioritise your budget. While factors like architecture and performance are important, your main focus should be on balancing them effectively with cost. Here's what you need to consider when choosing a GPU: 

Evaluate GPU Generations

Newer GPU models, such as NVIDIA's A100, L40 or H100, offer significant performance improvements over older generations like the NVIDIA RTX A6000. However, they also come at a higher price point. So, you must determine if the performance gains align with your application's requirements and if they justify the higher costs.

Cost Analysis

You should consider the total cost of ownership, including any additional fees for storage, networking, or data egress that may apply. Be wary of hidden costs associated with GPU usage on cloud platforms. This includes charges for networking, storage, and data transfer that can significantly affect the total cost. Hyperstack prides itself on transparency, offering ingress and egress at no extra cost, which can be a significant saving for data-intensive applications. Check out our cloud GPU pricing below.

Scalability and Flexibility

You must consider how easily and cost-effectively you can scale your GPU resources. For startups or projects with fluctuating demands, the ability to scale up or down without substantial cost penalties is crucial. You should look for programs like NVIDIA's Inception Program, which provides various benefits to AI startups, including a 20% lifetime discount on Hyperstack. As a cloud partner of the Inception Program, we help members leverage these discounts and perks. These programs can significantly help reduce costs for startups and scale-ups.

List of GPUs We Offer

Here is a quick list of high-end NVIDIA GPUs that we offer on our cloud platform in our comprehensive guide to GPUs:

• NVIDIA GB200 NVL72
• NVIDIA H100 SXM
• NVIDIA H100 PCIe
• NVIDIA A100
• NVIDIA L40
• NVIDIA RTX A6000
• NVIDIA RTX A5000
• NVIDIA RTX A4000

Conclusion

In conclusion, selecting the right GPU involves considering several key factors to ensure optimised performance for your specific needs. You must evaluate factors such as your intended use case, performance requirements, compatibility with software and frameworks, memory capacity, parallel processing capabilities, energy efficiency, and cost considerations. Also assessing display outputs and form factor limits ensures seamless integration into your system setup. Before making a final decision, we encourage you to conduct thorough research and gain insights into real-world performance and user experiences. By taking the time to understand how different specifications align with your requirements you can confidently select a GPU that delivers the performance and features you need for your applications, whether it's rendering, content creation, machine learning, scientific computing, or any other task.

Sign up for a free Hyperstack account today and lead innovations with the right GPU partner by your side!

FAQs

How to choose a GPU?

It's important to understand the basics of GPU architecture to assess your overall needs and performance requirements for the specific kind of workload you want to undertake. 

Which are the best machine learning GPUs?

NVIDIA H100 SXM, H100 PCIe and A100 are some of the best cloud GPU cards for deep learning.

How to select a GPU for rendering tasks?

For rendering tasks, consider leveraging CUDA Cores for efficient processing of complex calculations. For enhanced performance and realistic lighting effects, you must look for Ray Tracing Cores (RT Cores) to accelerate ray tracing calculations. To ensure the smooth handling of large textures and frame buffers, opt for memory architectures like high-speed GDDR or HBM.

Which is the best GPU for LLM?

NVIDIA A100 and the NVIDIA H100 are considered some of the best GPUs to tackle demanding LLM workloads. We also have a dedicated guide to selecting the right GPU for LLM. Check it out here.