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ACCELERATED COMPUTING

Accelerated infrastructure for AI, HPC, and graphics-intensive workloads.

Run demanding training, simulation, and inference jobs on enterprise-grade NVIDIA instances with high-throughput networking and predictable cost.

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Product Highlights

GPU Fleet

V100

Single and GPU clusters available

Highly Scalable

Run GPU Clusters

From a single GPU to GPU clusters

Savings

Up to 30%

Lower than AWS, GCP, and Azure

Reliability

99.99%

Uptime target
Single GPU Starting @ $0.63/hour
8 GPU Cluster Starting @ $5.04/hour
12 GPU Cluster Starting @ $7.50/hour
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NVIDIA DGX & Enterprise AI

Full DGX systems, custom GPU clusters, or colocate your own hardware in our Tier III+ datacenter.

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Hybrid & Multi-Cloud Ready

Avoid vendor lock-in. Train on RWS, deploy anywhere. Mix on-prem, colocation, and cloud seamlessly.

Learn about hybrid cloud
Proven Results & ROI

Up to 30% cost savings vs. hyperscalers. Real results from startups to research labs to enterprise teams.

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What is accelerated computing?

Accelerated computing uses specialized hardware processors to dramatically speed up computation-heavy workloads that would take much longer on traditional CPUs alone. By offloading parallel processing tasks to GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), or other accelerators, you can achieve 10x to 100x performance improvements for certain workloads.

While CPUs excel at sequential processing and general-purpose tasks, accelerators are designed for massive parallelism—processing thousands of operations simultaneously. This makes them ideal for AI/ML training, scientific simulations, data analytics, rendering, and other compute-intensive applications.

At RWS, we provide enterprise-grade GPU instances with the latest NVIDIA hardware, high-bandwidth networking, and flexible configurations—all at prices up to 30% lower than major cloud providers.

Why choose GPU acceleration?
Massive parallel processing

Modern GPUs have thousands of cores that can process multiple operations simultaneously, perfect for AI training and data processing

Drastically reduced training time

What takes weeks on CPUs can complete in hours or days with GPU acceleration

Higher throughput for inference

Serve more predictions per second for real-time AI applications

Better cost efficiency

Complete workloads faster means lower overall compute costs

Enterprise-grade AI infrastructure

From single GPUs to full NVIDIA DGX systems and custom AI clusters

NVIDIA DGX Systems

Deploy NVIDIA's purpose-built AI supercomputers—DGX A100, DGX H100, and DGX BasePOD configurations optimized for large-scale AI training and inference.

  • Pre-configured, validated AI infrastructure
  • Multi-GPU NVLink and NVSwitch connectivity
  • Optimized for distributed training
  • Enterprise support and SLAs

Colocation for AI Infrastructure

Need full control? Colocate your own DGX systems or custom AI clusters in our Tier III+ datacenter with redundant power, cooling, and high-speed networking.

  • High-density GPU rack support
  • Direct fiber connectivity options
  • Bring your own hardware or RWS-managed
  • 24/7 hands-on support available

Types of hardware accelerators

Different accelerators are optimized for different workloads

GPU (Graphics Processing Unit)

The most versatile accelerator, GPUs excel at parallel processing tasks. Originally designed for graphics rendering, modern GPUs like NVIDIA A100 and H100 are purpose-built for AI/ML workloads.

Best for:
  • Deep learning training and inference
  • Computer vision and image processing
  • Scientific simulations
  • Video transcoding and rendering
TPU (Tensor Processing Unit)

Google's custom-designed ASICs optimized specifically for tensor operations used in neural networks. TPUs offer superior performance for specific ML frameworks like TensorFlow.

Best for:
  • Large-scale neural network training
  • TensorFlow-based models
  • Natural language processing at scale
  • High-throughput inference
Multi-GPU Configurations

Scale your computing power with multiple GPUs working in parallel. Multi-GPU setups dramatically reduce training time for large models and enable processing of massive datasets that won't fit on a single GPU.

Best for:
  • Large language model training
  • Distributed deep learning
  • High-resolution video processing
  • Complex simulation workloads

At RWS, we primarily offer NVIDIA GPU instances which provide the best balance of performance, flexibility, and ecosystem support for most accelerated workloads.

Hybrid and multi-cloud AI deployments

Don't get locked into a single cloud provider. RWS enables hybrid and multi-cloud strategies that give you flexibility, avoid vendor lock-in, and optimize costs.

  • Train on RWS GPUs, deploy inference on AWS/GCP/Azure
  • Burst compute workloads to RWS during peak demand
  • Keep sensitive data on-prem while using cloud for preprocessing
  • Mix colocation, bare metal, and cloud instances seamlessly
Why hybrid AI infrastructure?
Data sovereignty & compliance

Keep regulated data on-premises while leveraging cloud for other workloads

Cost optimization

Use the most cost-effective infrastructure for each workload

Avoid vendor lock-in

Maintain portability across providers with containerized workloads

Performance where it matters

Low-latency inference at the edge, heavy training in the datacenter

Featured solutions

Why Choose RWS for Accelerated Computing?

At Redundant Web Services, we've engineered our accelerated computing platform specifically for high-performance projects that require significant computational power. Our accelerated computing resources are built on state-of-the-art infrastructure, offering up to 20% better performance compared to competitors at a fraction of the cost.

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Cost Effective Performance

Save up to 30% or more compared to other cloud providers while enjoying superior computing power.

100% Green Infrastructure

Our Columbia River location provides access to affordable hydroelectric power, allowing us to maintain sustainable operations while passing savings to customers.

Guaranteed Reliability

With our 100% uptime guarantee, your accelerated workloads will never experience unexpected downtime.

Simplified Management

Easily provision and manage your accelerated computing resources through our intuitive RWS Console.

Seamless Scalability

Scale your resources up or down based on your project requirements without long-term commitments.

Other high performance applications

Redundant Web Services (RWS) provides powerful solutions for handling your AI and Machine Learning Workloads through our state-of-the-art infrastructure and dedicated resources.

Scientific Computing

Run complex simulations and modeling for research and development

Data Processing

Handle massive datasets with optimized processing capabilities

Rendering

Accelerate 3D rendering for animation, visual effects, and architectural visualization

Financial Modeling

Process complex risk analyses and trading algorithms in real-time

Genomics

Analyze genetic sequencing data with remarkable speed

Accelerated computing on demand pricing

xlarge

$0.63 /hour
8xlarge

$5.04 /hour
12xlarge

$7.56 /hour
GPU's 1 8 12
vCPU 4 32 48
Memory 8 GB 64 GB 96 GB
Bandwidth Up to 10 GB Up to 10 GB Up to 10 GB

Accelerated computing FAQs

We offer a range of NVIDIA GPUs including the A100, H100, and V100 series. Each GPU is optimized for different workloads—A100 for general AI/ML tasks, H100 for large language models and cutting-edge research, and V100 for cost-effective training and inference. Contact our sales team to discuss which configuration best fits your needs.
Performance improvements vary by workload, but GPU acceleration typically delivers 10-50x faster training for deep learning models compared to CPUs. For some parallelizable tasks like image processing or matrix operations, you can see 100x or greater speedups. This translates to models that would take weeks on CPUs completing in hours or days on GPUs.
Our GPU instances support all major AI/ML frameworks including TensorFlow, PyTorch, JAX, Keras, MXNet, and more. We provide pre-configured images with CUDA, cuDNN, and popular frameworks already installed, or you can install your preferred stack. All NVIDIA software development tools and libraries are available.
Absolutely! We offer instances with 2, 4, or 8 GPUs connected via high-speed NVLink or NVSwitch for optimal multi-GPU training. This allows you to train larger models faster using data parallelism, model parallelism, or both. Our infrastructure supports distributed training across multiple instances for even larger scale projects.
GPU memory (VRAM) is dedicated high-bandwidth memory on the GPU used for storing model weights, activations, and intermediate computations. It's much faster than system RAM but typically smaller in size. For example, an A100 has 40GB or 80GB of GPU memory. Your model and batch size must fit within GPU memory, or you'll need to use techniques like gradient checkpointing or model sharding.
Yes! Our spot instances offer GPU compute at significantly discounted rates (up to 70% off) for interruptible workloads. Perfect for fault-tolerant training jobs with checkpointing, batch inference, or development and testing. You can configure automatic checkpointing to resume training if an instance is preempted.
GPU instances are typically ready in 2-5 minutes. You can choose from our pre-configured images with popular ML frameworks already installed, or bring your own Docker container. Once deployed, you have full root access via SSH and can start training immediately.
All GPU instances come with high-performance NVMe SSD storage for fast data loading. You can also attach network block storage for persistent datasets or connect to our object storage service for large-scale training data. We recommend keeping frequently accessed training data on local NVMe for maximum I/O performance.
Yes! Use nvidia-smi on your instance to monitor GPU utilization, memory usage, temperature, and power consumption in real-time. Our RWS Console also provides monitoring dashboards showing GPU metrics, allowing you to optimize your training jobs and ensure you're getting maximum value from your GPU resources.
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