Table of contents

What GPUs bring to AV devData in AV testingSimulation and synthetic dataTraining and inference at scaleGPU in AV infrastructureWhy GPU servers for AV testingRecommended GPUsPurchase vs rentHow to choose a GPU hosting providerNext steps

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GPU → Use Cases → Autonomous Vehicle Testing

Accelerating autonomous vehicle testing with GPU computing

Developing autonomous vehicles means constantly collecting, processing, and analyzing oceans of sensor data—while retraining models and running simulation environments that mimic the real world with millisecond precision. GPU computing isn’t just a nice-to-have for that—it’s a cornerstone. And if you’re not using GPU acceleration, you’re likely falling behind.

Let’s walk through exactly how GPU computing accelerates AV testing and where dedicated GPU servers give you the most control, speed, and cost-efficiency.

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What GPU computing brings to autonomous vehicle development

At its core, GPU computing leverages the massively parallel architecture of graphics processing units to run data-heavy workloads faster than traditional CPUs. Unlike CPUs, which have a few high-speed cores optimized for sequential tasks, GPUs can contain thousands of smaller cores that simultaneously execute computations across huge datasets.

This parallelism is what makes GPU computing ideal for:

Processing raw sensor streams: LiDAR point clouds, multi-angle camera feeds, and radar all produce real-time data that needs decoding, fusion, and interpretation—fast.
Running neural networks: CNNs, object detection models, semantic segmentation, and sensor fusion models all demand high-throughput compute for both training and inference.
Simulating environments: Rendering real-world physics, weather, traffic, and pedestrian behavior in tools like CARLA or NVIDIA DRIVE Sim requires constant GPU horsepower.

For AV teams, GPU computing makes it possible to test more scenarios, retrain models more often, and get to production faster.

The scale of data in AV testing

Autonomous vehicles are some of the most prolific data generators on the planet. A single AV test vehicle can produce between one to five petabytes of data per year, depending on the sensor stack and driving time.

Here’s where the data flood comes from:

Cameras: 6–12 cameras capturing 1080p or 4K video at 30–60 fps
LiDAR: High-res 3D point clouds that refresh 10–20 times per second
Radar & Ultrasonics: Supplemental spatial data with different depth characteristics
GPS + IMU: For precise localization and motion tracking

Processing this data fast enough to stay ahead of the vehicle pipeline is a real challenge. GPU compute helps with:

Preprocessing: Downsampling, noise reduction, sensor calibration, timestamp alignment—all handled in parallel
Annotation and labeling: AI-assisted tools can run on GPU to segment and label frames at scale, reducing human bottlenecks
Data filtering: Intelligent triage to find edge cases or unusual scenarios for model training

Without GPU acceleration, these steps either take too long or require compromise on dataset fidelity.

GPU acceleration for simulation and synthetic data

Physical testing is essential, but simulation is where you hit scale. Autonomous vehicle developers run millions of miles of synthetic testing before real-world deployment, because:

It’s safer. (You can test crashes or rare events.)
It’s faster. (You can simulate days in minutes.)
It’s repeatable. (You can test the same scenario hundreds of times.)

But to do that, you need high-performance compute that can handle:

Real-time rendering: Rendering environments in 3D with full physics and lighting for perception model testing
Physics simulation: Modeling object interactions, sensor responses, weather conditions, and complex agent behaviors
Data generation: Producing labeled datasets from simulated drives to feed into machine learning pipelines

GPU computing enables all of this. Tools like NVIDIA Omniverse, CARLA, and LGSVL rely on CUDA cores to simulate thousands of edge cases and generate diverse training data—especially for perception systems.

Training and inference at scale with GPU servers

The models powering autonomous vehicles aren’t “train once, deploy forever.” They need constant iteration, retraining, and validation. You’re feeding in petabytes of new data each week—and trying to compress that into faster, smarter decisions on the road.

GPUs speed up both sides of this:

Training: CNNs for lane detection, transformers for behavior prediction, RNNs for decision-making—training them on CPU would take weeks. On an A100, it might take hours.
Inference: Once a model is deployed, you still need to test and validate it in real-time, often across huge batches of data. GPUs let you run these models fast enough to support fleet-wide validation and regression tests.

GPU servers also let you experiment with frameworks like TensorFlow, PyTorch, and ONNX without worrying about provisioning limits. You control everything from drivers to optimization libraries.

Real-world AV infrastructure and where GPU servers fit

Most AV teams are building data infrastructure that looks something like this:

On-vehicle logging: Real-time sensor capture saved locally or streamed to base
Data lake: Massive object storage (cloud or on-prem) for raw data and labels
Processing + curation: GPU servers clean, index, and select training-worthy data
Training environment: GPU clusters train models on selected slices
Simulation: Synthetic environments evaluate performance in edge cases
Deployment: Final models pushed to embedded systems or AV stacks

GPU servers plug into steps 3 through 5. In some cases, they’re used for step 6 as well, especially when testing inference performance on edge-grade hardware.

Why bare metal GPU servers are a smart fit for AV testing

For autonomous vehicle testing, the type of GPU infrastructure you choose has a direct impact on performance, reproducibility, and cost. Bare metal GPU servers offer significant advantages over cloud GPUs and GPU as a Service (GPUaaS), especially once your workloads grow past the early experimental phase.

Here’s why bare metal is a smarter long-term play:

No virtualization overhead: Cloud GPUs are virtualized, meaning you share resources with other tenants. That introduces latency, throttling, and unpredictable performance—especially during peak hours. Bare metal gives you 100% of the GPU’s power, 100% of the time.
Consistent performance: Training and simulation workloads require high throughput and low jitter. With bare metal, you avoid the variability common in cloud instances, ensuring consistent test results, faster iteration cycles, and reliable benchmarking.
Customizability: Most cloud GPU platforms are locked into specific OS images, drivers, or frameworks. On bare metal, you control the stack—from BIOS to CUDA version to container engine—making it easier to optimize for your AV toolchain.
Better economics for persistent workloads: Cloud GPUs and GPUaaS shine for short, bursty jobs. But AV testing requires persistent infrastructure—multi-day training runs, 24/7 simulation farms, continuous data ingest and preprocessing. Bare metal pricing is fixed and transparent, with no surprise surcharges for extended usage or data egress.
Scalability with predictability: With bare metal, scaling means adding more servers—not jumping through reservation hoops or dealing with cloud instance shortages. You control the infrastructure roadmap—not your vendor.

Recommended server-class GPUs for AV workloads

Your choice of GPU depends on whether you’re focused on simulation, training, real-time inference, or a mix of all three. Liquid Web offers a range of high-performance GPU configurations purpose-built for AI/ML and large-scale data workloads:

NVIDIA H100: Designed for large-scale deep learning and transformer-based workloads, the H100 delivers unmatched performance for training AV perception, prediction, and planning models. Ideal for teams building complex multi-modal networks and retraining frequently.
NVIDIA H100 Dual GPU Server: If you’re processing petabyte-scale sensor data or running multi-node distributed training, this configuration gives you extreme throughput and memory bandwidth. Best for advanced AV teams pushing the limits of model complexity and retraining speed.
NVIDIA L40S: Purpose-built for simulation and 3D rendering workloads with AI-enhanced acceleration. Great for generating synthetic data or running perception stacks in real-time virtual environments.

If you’re running simulation, training deep neural networks, or handling petabyte-scale sensor data pipelines, bare metal GPU servers give you the control and performance you actually need—without the unpredictability or premium cost structure of the cloud.

Should you purchase or rent a bare metal GPU server?

Once you’ve committed to using bare metal GPUs for AV testing, the next decision is infrastructure ownership: do you purchase your own servers or rent them from a provider?

Reasons to consider renting:

Speed to deployment: No procurement delays, shipping, or racking time—spin up GPU servers in hours instead of weeks.
Operational flexibility: Scale up during peak development phases, scale down during post-deployment maintenance.
Zero maintenance overhead: No worrying about hardware failures, replacement parts, or firmware updates—your provider handles it all.
CapEx vs OpEx: Renting keeps infrastructure costs in the operational column, which may better align with budget planning and financial models.
Access to the latest hardware: Providers often upgrade server offerings faster than enterprise IT cycles, giving you newer GPUs without the sunk cost.

Reasons to consider buying:

Total control: If you need air-gapped systems or absolute hardware isolation, owning the stack may be preferred.
Lower costs over long timelines: If you plan to use a system continuously for 3+ years and can manage it in-house, ownership may work out cheaper over time.

That said, most AV teams, especially startups and R&D-focused units, start by renting bare metal GPU servers. It gives them the power and control of owning hardware, without the complexity or upfront investment.

How to choose a dedicated GPU server hosting provider

Not all hosting providers are equal. And for autonomous vehicle workloads, your GPU servers are mission-critical. Here’s what to look for:

Hardware selection that fits your stack: Make sure the provider offers modern, server-grade GPUs like the NVIDIA L40S, and H100. Avoid outdated or consumer-grade cards that can bottleneck your workflows.
Customizable configurations: AV workloads vary. Some teams need dual-GPU servers for training, others need high-memory nodes for simulation. Your provider should offer flexibility on RAM, storage (including NVMe), and networking.
Predictable pricing: Look for providers with flat-rate pricing and no surprise bandwidth charges or data caps. GPU workloads are heavy and constant, so surprises get expensive fast.
Responsive support: When something breaks or when you need help with a new driver stack, you want a provider with expert support that’s fast, technical, and available 24/7.
Strong uptime guarantees: AV testing infrastructure needs to be as stable as the vehicles themselves. Make sure your provider has SLAs that guarantee 99.99% uptime or better.

Next steps for GPU servers in autonomous vehicle testing

AV testing is compute-bound by nature. Whether you’re simulating edge cases, retraining detection models, or processing terabytes of sensor data per day, GPU servers unlock the power and scale required to keep up with development timelines and safety requirements.

For teams that need real performance, predictable cost, and infrastructure they can control, bare metal GPU hosting is the best path forward.

When you’re ready to upgrade to a dedicated GPU server, Liquid Web can help. Our dedicated server hosting options have been leading the industry for decades, because they’re fast, secure, and completely reliable. Choose your favorite OS and the management tier that works best for you.

Click below to explore dedicated GPU server options or start a chat with one of our experts to learn more.

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Chris LaNasa is Sr. Director of Product Marketing at Liquid Web. He has worked in hosting since 2020, applying his award-winning storytelling skills to helping people find the server solutions they need. When he’s not digging a narrative out of a dataset, Chris enjoys photography and hiking the beauty of Utah, where he lives with his wife.