Redundant Web Services Accelerated Computing Instances
The most cost-effective GPU instances for artificial intelligence, machine learning, and graphics-intensive tasks & applications.
Pay-Per-Use. Fast & Reliable.
Product highlights
- Latest NVIDIA GPUs (H100, A100)
- Deploy in under 60 seconds
- 30% cheaper than major clouds
Use specialized high performance hardware to dramatically speed up your work
Designed for applications or environments that require high processing capabilities
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
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.
How accelerated computing works
Understanding the architecture behind GPU-accelerated applications
Traditional CPU processing
Sequential execution
CPUs have 8-64 cores designed for complex, sequential tasks
One task at a time
Each core works on different parts of the problem serially
Limited parallelism
Training a large model could take weeks or months
GPU-accelerated processing
Massive parallelism
GPUs have thousands of smaller cores (CUDA cores) designed for parallel tasks
Simultaneous execution
Process thousands of operations at the same time
10x-100x faster
Same model trains in hours or days instead of weeks
When to use accelerated computing
Not every workload benefits from GPU acceleration. The sweet spot is applications that can parallelize operations across many data points simultaneously.
Excellent fit:
- • Training neural networks
- • Matrix operations
- • Image/video processing
- • Monte Carlo simulations
- • Molecular dynamics
Poor fit:
- • Sequential algorithms
- • Heavy I/O operations
- • Small datasets
- • Complex branching logic
- • Database queries
1000s
of CUDA cores per GPU
vs 8-64 CPU coresWhy 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.
Different Ways to Use RWS
Deep Learning
Train complex neural networks with faster iterations and reduced training time.
Natural Language Processing
Process and analyze large text datasets efficiently.
Computer Vision
Train and deploy image recognition and object detection models with speed and precision.
Generative AI
Create and fine-tune large language models and image generation algorithms.
Predictive Analytics
Build and deploy predictive models for business intelligence and forecasting.
Reach out to our team today to learn more. Contact us today!
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.
Run complex simulations and modeling for research and development
Handle massive datasets with optimized processing capabilities
Accelerate 3D rendering for animation, visual effects, and architectural visualization
Process complex risk analyses and trading algorithms in real-time
Analyze genetic sequencing data with remarkable speed
Accelerated computing on demand pricing
| 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 |
Contact our sales team to learn more and help with migrations
You can deploy Redundant Web Services Virtual Machine(VMs) in seconds. Run any workload, from mission critical CPU or memory intensive tasks to low traffic websites.
Ready to harness the power of accelerated computing for your projects? Sign up for a 30-day free trial of our RWS Console to start exploring our accelerated computing options. Our team is available to help you select the right configuration for your specific workload requirements.
Experience the perfect balance of performance, reliability, and cost-efficiency with RWS Accelerated Computing – purpose-built for the demands of modern AI and high-performance workloads.