GPU Performance

Metrics, Top GPUs, and How to Test Yourself

What Is GPU Performance?

GPU performance refers to how efficiently a graphics processing unit (GPU) can render graphics and perform complex calculations. High-performance GPUs process more data per second, resulting in smoother visuals and faster load times. They can handle high-resolution textures and detailed environments in real-time applications and games, and provide faster processing for AI and machine learning workloads.

Optimizing GPU performance involves balancing the workload between processing power and memory resources. GPUs with more cores, higher clock speeds, and larger memory capacities generally offer better performance. However, factors like driver efficiency and architecture are also important to consider.

This is part of a series of articles about multi GPU.

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Why Is GPU Performance Testing Important?

Testing GPU performance allows developers and users to understand a GPU's capabilities. Regular performance testing can also help in identifying potential bottlenecks and hardware issues.

Performance testing enables developers to optimize their applications or games to run smoothly across various devices. For data scientists and machine learning engineers, it lets them understand how long it will take to train a complex model or run inference on a production model. For users, it's a way to gauge whether a GPU can handle specific tasks, like gaming at high resolutions or professional graphic work.

Metrics for Evaluating Your GPU Performance

There are several ways to measure the performance of a GPU.

GPU Utilization

GPU utilization measures how much of the GPU's processing capacity is being used. High utilization indicates that a GPU is being sufficiently leveraged, whereas low utilization suggests a bottleneck elsewhere in the system, such as the CPU or memory bandwidth.

Monitoring utilization over time helps in identifying patterns of resource usage. This insight can guide optimizations, ensuring that applications use the GPU efficiently without unnecessary idle times.

GPU Memory Access and Usage

Memory access patterns can significantly affect GPU performance. GPUs with faster and more efficient memory access can handle larger datasets and textures more effectively. Memory usage indicates how much of the GPU's memory is being utilized.

Balancing memory access and usage is helpful for avoiding performance drops. Excessive memory usage can lead to swapping, significantly slowing down GPU operations, while inefficient access patterns can lead to underutilization of the GPU's computational resources.

Power Consumption and Temperature

Higher performance often results in increased power usage and higher temperatures. Efficient cooling mechanisms are crucial to maintain optimal performance levels and prolong the lifespan of the GPU.

Monitoring these metrics enables adjustments to be made to achieve a balance between performance, power consumption, and temperatures, ensuring sustainable long-term GPU performance.

Clock Speed

Clock speed determines how many processing cycles a GPU can execute per second. Higher clock speeds allow for more operations to be performed, improving overall performance. However, this also typically increases power consumption and heat generation.

Adjusting clock speeds, a practice known as overclocking, can boost performance. However, it requires careful monitoring to avoid overheating and potential hardware damage.

Best 5 GPUs by Performance

Here’s an outline of the five best-performing GPUs, according to tests carried out by Tom’s Hardware. We summarize the key findings below.

RTX 4090

The GeForce RTX 4090 delivers unmatched performance, especially in high-resolution settings like 4K. With its AD102 architecture, it features 16,384 shaders and 24GB of GDDR6X memory, facilitating throughput of 1008GB/s. This makes it an ideal choice for demanding tasks that include advanced AI workloads, detailed 4K gaming, and intensive professional rendering. However, it’s important to note that the 4090 is also power-hungry, with a consumption of 450W.

Radeon RX 7900 XTX

The Radeon RX 7900 XTX, equipped with the RDNA 3 architecture, provides 6,144 shaders and 24GB of GDDR6 memory, with memory bandwidth of 960GB/s. It excels in 1440p and comes close to matching the RTX 4090 in various 4K settings.

This GPU is particularly suitable for gamers and professionals seeking top-tier performance, and is significantly cheaper than the RTX 4090, though it slightly lags behind in the highest resolution tasks.

GeForce RTX 4080 Super

The GeForce RTX 4080 Super provides a balance of performance and efficiency. This model is built on the AD103 chip with 10,240 shaders and 16GB of GDDR6X memory, with a bandwidth of 736GB/s. It's particularly noted for its efficiency at 1440p, making it a good choice for high-end gamers who also consider power consumption and thermal performance.

The 4080 Super offers a compelling argument for users who need high performance but are looking to manage cost and energy more effectively than the 4090 allows.

GeForce RTX 4080

With performance only slightly lower than the 4080 Super, the GeForce RTX 4080 is built with the same AD103 architecture but slightly reduced specs, featuring 9,728 shaders and the same 16GB GDDR6X memory. This card excels at 1440p and provides robust 4K performance, making it suitable for users who want near-top-tier performance but at a lower price than the 4080 Super.

Its performance in detailed gaming and intensive applications is strong, though users might notice minor compromises in the most demanding scenarios.

Radeon RX 7900 XT

The Radeon RX 7900 XT offers excellent performance at a more accessible price point. It uses the RDNA 3 architecture with 5,376 shaders and 20GB of GDDR6 memory, with 800GB/s of memory bandwidth.

While it doesn't match the performance of the 4090 or 7900 XTX, its performance in 1440p and reasonable 4K capabilities make it a strong contender for users needing solid performance without the premium price of the higher-end models.

Testing GPU Performance Yourself: Top GPU Benchmarks

Benchmarks can be used to evaluate the performance of GPUs based on established standards.


3DMark offers various tests tailored to different types of devices and performance levels. From basic mobile graphics to high-end gaming PCs, 3DMark assesses GPU performance across a spectrum of scenarios.

This benchmarking suite can compare scores with a large database of other systems, making it a useful tool for evaluating a GPU's performance against competitors and identifying areas for improvement.

Basemark GPU

Basemark GPU is a cross-platform benchmark tool that measures GPU performance across various operating systems and devices. It tests graphics and compute capabilities to provide a comprehensive overview of a GPU's performance.

With specific tests designed for high-end gaming graphics and general GPU compute tasks, Basemark GPU helps users understand the strengths and weaknesses of their GPUs in different contexts.

Superposition Benchmark

Superposition Benchmark from UNIGINE offers detailed tests that push GPUs to their limits, focusing on rendering high-quality visuals and physics simulations. It's particularly useful for evaluating a GPU's performance in gaming and VR scenarios.

Its detailed score breakdowns and the ability to run at various resolutions make Superposition suitable for assessing how a GPU handles graphically intensive tasks under different conditions.


Cinebench primarily tests CPU performance but includes a GPU test that evaluates the capability of the GPU to render high-quality images. It's useful for users who want to understand how their system's GPU and CPU work together under load.

Cinebench's straightforward scoring system allows for easy comparison between different systems. It’s a convenient tool for gauging overall system performance, including the GPU's role in rendering tasks.

Multi GPU Processing With Run:ai

Run:ai automates resource management and workload orchestration for machine learning infrastructure. With Run:ai, you can automatically run as many deep learning experiments as needed on multi-GPU infrastructure.

Here are some of the capabilities you gain when using Run:ai:

  • Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
  • No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
  • A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.

Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.

Learn more about the Run:ai GPU virtualization platform.