Top Machine Learning Benchmarks for GPU Performance

Are you a retailer aiming to predict customer demand and optimize inventory for maximum profitability? Or perhaps you’re in healthcare, seeking to accelerate genomic analysis or enhance image processing to improve diagnostics? Maybe you’re in manufacturing, looking to streamline quality control and anticipate maintenance needs before they impact production. For all these data-intensive projects, the right GPU can be a game-changer. 

But with so many options on the market, how do you decide which machine learning benchmarks for GPU performance matter most for your industry? This guide breaks down the key metrics that can help you choose the ideal GPU for your machine learning needs, unlocking insights faster and more efficiently.

We’ll also share learnings on maximizing your GPU investment to ensure you’re fully leveraging its capabilities for faster, more cost-effective data processing.

What Are Machine Learning Benchmarks for GPUs?

Benchmarking plays a critical role in evaluating GPU performance for ML workloads, measuring essential metrics like FLOPS, memory bandwidth, and training times to help data scientists and engineers make informed choices. 

At a high level, machine learning benchmarks for GPUs are standardized tests that assess a GPU’s performance on various ML tasks, like deep learning model training, data processing, and inferencing. 

Benchmarks show GPU capabilities, from handling parallel computations to memory efficiency, providing insights into how a GPU performs in real-world ML applications. 

These benchmarks allow organizations and developers to choose GPUs that align best with their performance, cost, and power needs, helping accelerate insights and streamline computationally heavy tasks.

Key Metrics to Consider in GPU Benchmarks for Machine Learning

1. Floating-Point Operations per Second (FLOPS): A GPU’s FLOPS measure its computing power, crucial for ML tasks that require extensive matrix computations. Higher FLOPS usually translate to faster model training and inference.
2. Memory Bandwidth: This metric indicates how quickly data can move to and from the GPU memory, impacting performance in data-intensive tasks.
3. Latency and Throughput: These benchmarks assess how efficiently a GPU can manage multiple concurrent tasks.
4. Training Time: For ML workloads, training time benchmarks measure how long a GPU takes to train standard models like ResNet or GPT. This helps gauge the GPU’s practical efficiency in model training.

Best GPUs for Machine Learning in 2025 Based on Benchmarks

With a variety of GPUs expected to dominate machine learning workflows in 2025, here’s a breakdown of key machine learning benchmarks for popular GPU options, helping you match GPU performance to your ML needs:

1. NVIDIA A100

• FP32 (Single-Precision) Performance: Up to 19.5 teraflops (TFLOPS), essential for applications that require single-precision floating-point operations.
• FP16 (Half-Precision) Performance: Up to 312 TFLOPS with Tensor Cores, highly beneficial for deep learning tasks that can tolerate reduced precision.
• Memory Bandwidth: 1.6 TB/s, supporting fast data transfer, crucial for large-scale data processing.
• GPU Memory: 40GB or 80GB HBM2, designed for handling large datasets in complex ML models.
• Benchmark Result Highlights: High throughput on MLPerf benchmarks, especially in natural language processing (NLP) and image recognition tasks, where low latency and high performance are key.

2. NVIDIA H100

• FP32 Performance: Up to 60 TFLOPS, delivering substantial improvement in single-precision workloads compared to previous models.
• FP16 Performance with Tensor Cores: Up to 1,000 TFLOPS, ideal for compute-intensive tasks like training massive neural networks.
• Memory Bandwidth: 3.35 TB/s, enabling rapid data movement within the GPU.
• GPU Memory: 80GB HBM3, providing an advantage for training larger models and more complex simulations.
• Benchmark Result Highlights: Top scores in MLPerf training and inference benchmarks, particularly excelling in NLP, recommendation models, and transformer-based architectures.

3. AMD Instinct MI250X

• FP64 (Double-Precision) Performance: 95.7 TFLOPS, a unique strength for applications that rely on double-precision accuracy.
• FP32 Performance: 383 TFLOPS, making it highly competitive for ML applications needing intense computation.
• Memory Bandwidth: 3.2 TB/s, supporting high-throughput ML workloads.
• GPU Memory: 128GB HBM2e, the largest in its class, suitable for data-heavy applications such as large language models.
• Benchmark Result Highlights: Strong performance on deep learning benchmarks, especially in data-parallel and multi-GPU workloads, showing impressive scalability in distributed training.

4. Intel Data Center GPU Max Series

• FP32 Performance: Estimated at 20 TFLOPS, positioned for general ML workloads and enterprise-level applications.
• FP16 Performance: Optimized for AI inferencing, with performance metrics competitive for machine learning deployments requiring consistent, lower precision.
• Memory Bandwidth: Around 1 TB/s, which supports data-driven analytics and medium-scale ML workloads.
• GPU Memory: 64GB GDDR6, providing ample memory for most enterprise ML tasks.
• Benchmark Result Highlights: Benchmarked well for data preprocessing and inference tasks, making it a suitable choice for organizations focused on cost-effective, mid-range ML processing.

Now that we’ve looked at some key machine learning benchmarks for leading GPUs, we can turn our attention to cloud versus on-prem benchmark results. 

Comparing Cloud vs. On-Premise GPU Benchmark Results

When it comes to ML benchmarking, choosing between cloud-based and on-premise GPUs impacts performance and cost:

• Cloud GPUs: Cloud providers offer flexibility with pay-as-you-go models and ready access to high-performance GPUs. For rapid ML testing or short-term projects, cloud GPUs provide scalability without significant upfront investment. 
• On-Premise GPUs: For enterprises requiring constant high-volume data processing, on-premise GPU setups, while costly initially, can provide lower long-term costs. In many instances they also allow better control over data security and customization for specific ML workflows.

For both deployment types, getting the most out of your GPU performance is about more than the GPU itself; having the right data analytics and acceleration platform is a critical part of getting the results you want – both in terms of performance and cost. 

Benchmarking GPU Performance in Real-World Machine Learning Tasks

Different ML tasks stress GPUs in unique ways, so it’s essential to consider benchmarks tailored to specific workloads. For example:

• Deep Learning Model Training: Benchmarks for models like BERT, ResNet, and GPT-3 measure training efficiency and memory utilization. High FLOPS and memory bandwidth improve these benchmarks, making models like the NVIDIA A100 or H100 ideal for complex NLP tasks.
• Data Processing and ETL: Tasks such as ETL (extract, transform, load) demand high memory bandwidth and efficient data handling. GPUs with high memory bandwidth are well-suited for such use cases, especially in large-scale data preprocessing.
• Inference Applications: For applications like facial recognition or autonomous vehicle control, benchmarks focus on latency and throughput. Low latency GPUs like the NVIDIA RTX 3090 are effective here due to quick inference times.

Each task requires different benchmark parameters, enabling ML practitioners to choose GPUs that fit their precise computational needs.

FAQs

Q: How does multi-GPU benchmarking work in machine learning?

A: Multi-GPU benchmarking evaluates how GPUs perform when distributed across multiple devices. It’s vital in scaling workloads across many GPUs, where factors like inter-GPU bandwidth and workload balancing are assessed. Tools like NVIDIA NVLink and AMD Infinity Fabric enhance multi-GPU performance by improving inter-GPU communication.

Q: How can I improve the performance of my GPU in machine-learning tasks?

A: To improve GPU performance in machine learning tasks, streamline data preparation and reduce processing bottlenecks. A solution like SQream’s data acceleration platform, which harnesses GPU technology, enables organizations to handle terabyte- and petabyte-scale datasets while running complex queries more efficiently and affordably. With built-in data compression, seamless integration, and parallel processing across multiple cores, it reduces the total cost of ownership and accelerates insights, empowering organizations to make the most of their GPU investments.

Q: How do I run machine learning benchmarks on my GPU?

A: Popular benchmarking tools like MLPerf provide standard ML benchmarks. Frameworks like TensorFlow and PyTorch also include built-in benchmarking options for tasks like training and inference, providing metrics for throughput and processing efficiency.

Q: Are older GPUs still relevant for machine learning benchmarks?

A: Yes, older GPUs remain effective for lighter ML tasks or smaller datasets. While they may not compete with newer models on metrics like FLOPS, they still offer solid performance for entry-level ML tasks and model inferencing.

Meet SQream: GPU-Accelerated Data Processing for ML Workloads

SQream revolutionizes how organizations process and analyze massive datasets, offering unparalleled speed, scalability, and cost efficiency through its advanced GPU-accelerated technology. As data volumes grow to terabytes and even petabytes, traditional data platforms often struggle to keep up – leading to delays, increased costs, and missed opportunities. 

SQream overcomes these limitations by harnessing the power of GPUs to perform complex, high-volume analytics faster and more affordably than conventional solutions.

Designed to seamlessly integrate with existing data ecosystems, SQream’s platform allows organizations to unlock insights from even the most complex queries without extensive hardware scaling. By processing data in parallel across GPU and CPU resources, SQream minimizes latency and significantly reduces the total cost of ownership, making it ideal for data-heavy industries like finance, telecommunications, and healthcare.

In addition to rapid analytics and ML capabilities, SQream offers unmatched flexibility with deployment options in the cloud or on-premise. This versatility enables organizations to maintain data privacy and control while benefiting from the efficiency and scalability of GPU acceleration. With easy integration into data pipelines and support for industry-standard connectors, SQream simplifies big data analytics, empowering teams to make data-driven decisions more swiftly and cost-effectively.

Trusted by leading enterprises worldwide, SQream’s holistic approach to GPU acceleration is essential for today’s data-driven enterprises, empowering them to access high-powered processing and to achieve a whole new level of performance, scalability, and competitive advantage that goes far beyond the hardware itself​​​​.

Conclusion: Using Machine Learning Benchmarks To Maximize Value from GPUs

Selecting the right GPU hinges on understanding machine learning benchmarks for GPU performance that match your specific use case. 

By leveraging powerful GPUs and understanding benchmark results, businesses can drive their ML and AI initiatives with efficiency and cost-effectiveness. 

For enhanced results, consider SQream’s solution to elevate your data capabilities and achieve superior insights at scale. Get in touch with the SQream team to learn more.