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GPU servers for big data analytics
Data analysis is more demanding than ever, with professionals processing massive datasets, running complex machine learning models, and performing high-speed computations. Traditional CPU-based computing often struggles to keep up.
That’s where GPUs come in. Originally designed for gaming and graphics rendering, GPUs now play a crucial role in accelerating data analytics. Let’s explore how they work and why they’re an essential tool for modern data professionals.
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What is a GPU?
A GPU, or graphics processing unit, is a specialized circuit designed to quickly process and manipulate computer graphics and image data in parallel. The GPU offloads these compute-intensive tasks from the central processing unit (CPU), allowing the CPU to handle more generic computational workloads.
GPUs have thousands of cores that work together to process large amounts of data efficiently. This parallel architecture enables GPUs to perform mathematical computations at speeds far beyond what CPUs can achieve. As a result, they are increasingly used in scientific computing, financial modeling, and big data analysis.
Benefits of GPUs for data analytics
- Massive parallelism: GPUs process thousands of operations simultaneously, making them significantly faster for tasks like matrix operations, statistical modeling, and machine learning.
- Faster processing speeds: Compared to CPUs, GPUs can accelerate data transformation and analysis, reducing the time needed to process large datasets.
- Efficient handling of large datasets: GPUs can quickly perform operations on massive datasets, making them essential for deep learning, predictive analytics, and big data applications.
- Optimized for AI and ML workloads: Machine learning and deep learning models require extensive calculations that GPUs can handle more efficiently than CPUs.
- Better scalability: GPU server hosting allows organizations to scale computing power up or down as needed without investing in expensive on-premise infrastructure.
GPU vs CPU for data analysis
CPUs and GPUs both play critical roles in data analysis, but they are optimized for different workloads. Here’s a quick comparison:
| Feature | CPU | GPU |
| Processing type | Sequential | Parallel |
| Core count | Dozens | Thousands |
| Speed for large-scale data analysis | Slower | Faster |
| Optimization for machine learning | Limited | Highly optimized |
| Energy efficiency | Higher power consumption | More efficient for parallel tasks |
| Scalability | Limited by hardware | Scalable with cloud hosting |
CPUs are still essential for tasks requiring complex logic, real-time processing, and general computing. However, for workloads involving massive datasets and AI-driven analytics, GPUs offer a significant advantage.
How to choose a GPU for data science
Selecting the right GPU for data science isn’t just about picking the most powerful option. Your choice should align with your specific workload, budget, and future scaling needs. Below are key factors to consider when choosing a GPU for data science.
1. GPU memory
GPU memory determines how much data your GPU can process at once. Large datasets, complex neural networks, and high-resolution simulations require significant memory capacity. If your datasets exceed the GPU’s memory, computations will slow down as the system offloads tasks to slower CPU memory or disk storage.
Deep learning models like transformer-based NLP applications need at least 24GB of VRAM to train efficiently. If you’re working with smaller tabular datasets or traditional statistical models, 8 to 12GB might be sufficient.
2. CUDA cores and tensor cores
CUDA cores (Compute Unified Device Architecture) handle parallel computations, while tensor cores (found in NVIDIA GPUs) accelerate deep learning operations, such as matrix multiplications. More CUDA cores improve general parallel processing, speeding up traditional data analytics workloads. Tensor cores significantly boost machine learning model training and inference speeds.
If you’re training deep learning models, NVIDIA GPUs with tensor cores (like the RTX 4090 or A100) offer superior performance. For general-purpose data analysis, a GPU with a high CUDA core count, such as an RTX 3090 or A6000, works well.
3. FP32 and FP16 performance
Floating point operations per second (FLOPS) determine how fast a GPU can perform mathematical calculations. FP32 (single-precision) is critical for traditional data science workloads, while FP16 (half-precision) is optimized for deep learning. Higher FP32 performance ensures better handling of numerical simulations, while FP16 performance speeds up AI model training.
If you’re working with numerical simulations in physics or finance, a GPU with high FP32 performance (like the NVIDIA A100) is a must. For deep learning, FP16 acceleration in GPUs like the RTX 4090 reduces training time without sacrificing accuracy.
4. Energy efficiency and power consumption
GPUs consume different amounts of power, affecting operational costs and cooling requirements. High-performance GPUs require more wattage and may need better cooling solutions. If power consumption is a concern, opt for GPUs that balance performance and efficiency. High-end data center GPUs (like the NVIDIA A100 or H100) are designed for optimized power usage.
If you’re running a cloud-based machine learning pipeline, you may prefer GPUs with good performance-per-watt ratios, such as the NVIDIA L4S.
5. Compatibility with data science frameworks
Popular data science frameworks like TensorFlow, PyTorch, RAPIDS, and CUDA require specific GPU support. Some older GPUs may not be compatible with newer libraries. If your GPU isn’t optimized for modern frameworks, you may experience software compatibility issues or reduced performance.
If you’re using RAPIDS for GPU-accelerated data analytics, an NVIDIA GPU with strong CUDA support, like the RTX 6000 Ada or H100, is essential.
6. Scalability and multi-GPU support
Some workloads require multiple GPUs working together for parallel processing. Certain GPUs and architectures (like NVLink) allow for better scalability. If your work demands scaling across multiple GPUs, choose models with NVLink support and high interconnect bandwidth.
If you’re training large AI models like diffusion models or running distributed computing tasks, the NVIDIA H100 or A100 in a multi-GPU setup is ideal.
7. Cloud vs on-premises deployment
Deciding between GPU hosting and on-premises deployment depends on your budget, workload flexibility, and long-term infrastructure goals.
- On-premises GPUs require a significant upfront investment in hardware, cooling, and maintenance, but they offer complete control and may be cost-effective in the long run.
- GPU hosting provides on-demand access to high-performance hardware without the hassle of maintenance, making it ideal for dynamic workloads.
If you’re running consistent, high-volume workloads and want full control over your infrastructure, investing in on-premises GPUs may make sense. If you need scalability, flexibility, and access to enterprise-grade GPUs (like the A100 or H100) without upfront costs, GPU hosting is the better choice.
Best GPUs for data analysis
Not all GPUs are built the same, and choosing the right one for data analytics depends on your specific workload. Whether you’re handling machine learning models, real-time data processing, or large-scale simulations, the right GPU can significantly accelerate your workflow. Below are three top-tier NVIDIA GPUs that excel in data analysis.
- NVIDIA L4 Ada: A power-efficient GPU optimized for AI inference, real-time analytics, and machine learning. Best for AI-driven data processing and cost-effective cloud workloads.
- NVIDIA L40S Ada: A high-performance GPU with 48GB of memory, ideal for large-scale data science, financial modeling, and advanced analytics. Best for businesses needing a balance of compute power and AI acceleration.
- NVIDIA H100 NVL: A dual-GPU powerhouse with 188GB HBM3 memory and NVLink for massive parallel processing. Best for enterprises handling big data, AI training, and large-scale predictive analytics.
How to choose GPU server hosting for data analytics
Instead of purchasing and maintaining expensive GPU servers, many organizations opt for GPU hosting services. Hosting providers offer flexible, scalable, and cost-effective solutions that eliminate the overhead of hardware maintenance.
Key considerations for GPU hosting:
- Performance and GPU availability: Ensure the provider offers high-end GPUs optimized for data science workloads.
- Scalability and pricing: Choose a service that allows you to scale resources up or down as needed, with transparent pricing models.
- Framework and software support: Verify compatibility with essential tools like TensorFlow, PyTorch, and CUDA libraries.
- Network speed and latency: High-speed connectivity is critical for large dataset transfers and distributed computing.
- Reliability and uptime: Look for providers with strong uptime guarantees and redundant infrastructure.
Getting started with GPU server hosting
GPUs have transformed data analytics by delivering massive parallel processing power, accelerating workloads that CPUs struggle to handle efficiently. When selecting a GPU for data science, key factors include memory size, core performance, bandwidth, and scalability.
First, decide if you need to invest in hardware or find the best GPU hosting provider. While purchasing on-premises GPUs offers full control, GPU hosting provides flexibility and scalability without the high upfront costs.
When you’re ready for the best GPU server hosting available, Liquid Web can help. We offer GPU servers with L40S and H100 chips. On top of the premium hardware, you get the best uptime guarantees, server security, and customer support in the industry.
Click below to explore GPU server hosting options or start a chat right now to talk to one of our team members.
Additional resources
Best GPU server hosting [2025] →
Top 4 GPU hosting providers side-by-side so you can decide which is best for you
NVIDIA L40 vs L40S →
A side-by-side comparison of the L40 and L40S chips, so you can decide which is right for you.
GPU for AI →
How it works, how to choose, how to get started, and more
Brooke Oates is a Product Manager at Liquid Web, specializing in Cloud VPS and Cloud Metal, with a successful history of IT/hosting and leadership experience. When she’s not perfecting servers, Brooke enjoys gaming and spending time with her kids.