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About

Charg is an AI infrastructure lifecycle platform that transforms proven enterprise-grade supercomputing systems into scalable AI and high-performance computing cloud environments. Its public HPC cloud provides access to anything from a single GPU to a full 60+ PFLOPS cluster, giving teams supercomputing power without owning or managing the underlying hardware. It redeploys hyperscaler-class CRAY supercomputers and mature NVIDIA DGX architecture, combining clustered NVIDIA V100 GPUs with 200 GbE InfiniBand networking and petabytes of high-density all-flash CEPH storage for low-latency, high-throughput performance. Charg is built for demanding AI, scientific research, and engineering workloads, including model training, scaled inference, simulations, advanced data analysis, finite element analysis, and computational fluid dynamics. Its API-driven infrastructure scales with existing workflows and supports on-demand capacity without the operational restrictions.

About

GPUniq is a decentralized GPU cloud platform that aggregates GPUs from multiple global providers into a single, reliable infrastructure for AI training, inference, and high-performance workloads. The platform automatically routes tasks to the best available hardware, optimizes cost and performance, and provides built-in failover to ensure stability even if individual nodes go offline. Unlike traditional hyperscalers, GPUniq removes vendor lock-in and overhead by sourcing compute directly from private GPU owners, data centers, and local rigs. This allows users to access high-end GPUs at up to 3–7× lower cost while maintaining production-level reliability. GPUniq supports on-demand scaling through GPU Burst, enabling instant expansion across multiple providers. With API and Python SDK integration, teams can seamlessly connect GPUniq to their existing AI pipelines, LLM workflows, computer vision systems, and rendering tasks.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

University research labs that need scalable supercomputing resources for AI training, scientific simulations, and data-intensive engineering

Audience

AI startups, machine learning engineers, data scientists, AI researchers, computer vision teams, 3D artists, game developers, and tech companies building AI-powered products.

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

$0.99 per hour
Free Version
Free Trial

Pricing

$5/month
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 5.0 / 5
ease 4.0 / 5
features 5.0 / 5
design 4.0 / 5
support 5.0 / 5

Pros & Cons from Real Users

Pros

  • GPUniq makes it extremely easy to find and compare GPU providers in one place. The interface is clean, performance metrics are clear, and pricing is transparent. I especially liked how quickly I could deploy a GPU without dealing with multiple vendors separately.

Cons

  • Some providers have limited availability during peak hours, and I would like to see more advanced filtering options for very specific GPU configurations.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Charg
United States
charg.cloud/

Company Information

GPUniq
Founded: 2025
United Arab Emirates
gpuniq.com

Alternatives

Lambda

Lambda

Lambda.ai

Alternatives

Categories

Categories

Integrations

No info available.

Integrations

No info available.
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