GMI Cloud provides high-performance GPU cloud solutions for scalable AI training, inference, and deployment. It combines an inference engine for low-latency model serving, a cluster engine for orchestration, and on-demand access to top-tier NVIDIA GPUs like H100 and H200. The platform helps reduce costs, boost performance, and accelerate AI development with flexible pricing and enterprise-grade infrastructure.
Paid
$2.10/GPU-hour
How to use GMI Cloud?
Users can start by signing up and accessing the console to deploy GPU instances for AI workloads. The Inference Engine allows deploying and scaling large language models with automatic scaling for real-time inference. The Cluster Engine simplifies container management and orchestration for distributed training. Users can choose between on-demand or reserved GPU pricing, integrate with frameworks like TensorFlow and PyTorch, and monitor operations through a real-time dashboard. This enables faster model development, efficient resource utilization, and seamless production deployment.
GMI Cloud 's Core Features
High-performance GPU access with NVIDIA H100, H200, and upcoming Blackwell platforms, optimized for large models and data-intensive tasks, delivering faster training and inference with ultra-high memory bandwidth.
Inference Engine 2.0 provides ultra-low latency and automatic scaling for AI model serving, enabling real-time predictions and efficient deployment of LLMs in production environments.
Cluster Engine offers Kubernetes-based GPU containerization and orchestration, streamlining workload management, container deployment, and secure networking for scalable AI operations.
Flexible pricing models including on-demand and reserved GPUs, with pay-as-you-go options and volume-based discounts, allowing cost optimization without long-term commitments.
Enterprise-grade infrastructure with InfiniBand networking, Tier-4 data centers, and support for frameworks like TensorFlow and PyTorch, ensuring reliability, security, and high throughput.
Real-time dashboard and monitoring tools provide full visibility into AI operations, enabling instant insights, performance tracking, and granular access management for teams.
Model Library and demo apps offer pre-built AI models and applications, facilitating experimentation and faster time-to-market for various use cases.
GMI Cloud 's Use Cases
AI researchers and data scientists can accelerate model training and fine-tuning on high-performance GPUs, reducing development time and costs while handling large datasets efficiently.
Startups and tech companies deploying production AI applications benefit from the Inference Engine's low-latency serving and automatic scaling, ensuring reliable performance under variable loads.
Enterprises managing complex AI workflows use the Cluster Engine for orchestration, simplifying container management and enabling seamless collaboration across distributed teams.
Video production studios like Utopai leverage elastic GPU clusters to enhance creative quality, cut costs by 50%, and scale cinematic generative video projects efficiently.
AI infrastructure providers partner with GMI Cloud to reduce inference latency by 65% and lower compute costs by 45%, as seen with Higgsfield's studio-quality video tools.
Developers building LLM-based applications utilize the Model Library and free endpoints to experiment with reasoning models, speeding up prototyping and innovation.
Educational institutions and research labs access on-demand GPUs for HPC workloads, supporting large-scale simulations and academic projects without upfront investments.
GMI Cloud 's Pricing
Reserved GPUs
As low as $2.50/GPU-hour
Fixed, committed capacity for production workloads with long-term commitment, offering guaranteed scale and stable, predictable cost.
On-demand GPUs
Starting at $4.39/GPU-hour
Pay-as-you-go for fine-tuning and experimentation with short-term flexibility, providing burstable capacity and maximum adaptability.
