Dihuni Launches GPU Cloud to Empower AI Workloads
Dihuni, a provider of AI, IoT, and data center solutions, today unveiled its GPU Cloud, rebranding the advanced Qubrid AI platform under its own brand. The offering provides GPU as a Service (GPUaaS), AI tools, and flexible deployment options for enterprises, startups, and researchers.
“Our GPU Cloud extends over seven years of on-premises GPU expertise to the cloud, giving customers access to cutting-edge infrastructure without the capital expense of purchasing hardware,” said a company spokesperson.
On-Demand GPU Access for Faster AI Innovation
The Dihuni GPU Cloud allows organizations to spin up dedicated GPUs on-demand, accelerating AI training, fine-tuning, inferencing, and RAG workflows. Users can choose between cloud, on-premises, or hybrid deployments depending on scalability, security, and budget needs.
Key features include:
- On-Demand GPU Compute: Advanced GPU virtual machines for AI and high-performance workloads.
- Long-Term GPU Server Rentals: Dedicated bare-metal servers available for monthly or annual periods.
- AI Inferencing Pipelines: Scalable deployment and optimization for production-ready AI models.
- Retrieval-Augmented Generation (RAG): Enterprise-ready workflows combining knowledge retrieval with generative AI.
- Hybrid Deployment Options: Cloud, on-premises, or hybrid flexibility via Qubrid AI controller software.
- Transparent GPU Allocation: Dedicated GPU access without oversubscription or hypervisor inefficiencies.
By leveraging Qubrid AI’s next-generation platform, Dihuni offers turnkey AI templates, enterprise-ready infrastructure, and optimized performance, helping organizations accelerate time-to-market for AI initiatives.
With this launch, Dihuni positions itself as a flexible alternative to hyperscale GPU providers, giving businesses and research institutions a scalable, transparent, and enterprise-ready GPU cloud platform for modern AI workloads.
Power Tomorrow’s Intelligence — Build It with TechEdgeAI
