GMI Cloud

GMI Cloud is an AI infrastructure platform specifically designed for the deployment, inference, and management of agents in production environments. As a preferred NVIDIA partner, this solution unifies access to high-performance GPU clusters and optimized runtimes into a single cloud, enabling companies and developers to manage AI workloads at scale without the complexities of traditional architecture.

The tool operates with a serverless inference by default approach, allowing AI models to be executed instantly. Its technology enables auto-scaling to zero, eliminating idle costs and optimizing resource usage through integrated batch processing and latency-aware scheduling. For projects requiring total control, GMI Cloud facilitates the transition to dedicated GPU infrastructure through bare metal servers based on NVIDIA’s reference cloud architecture, utilizing advanced hardware such as NVIDIA H100, H200, and Blackwell units.

Key capabilities and functional benefits include:

• Unified API: Simplified access to a library of Large Language Models (LLMs) and multimodal models, including options like DeepSeek, MiniMax, and Gemini.
• Optimized Performance: The ability to achieve up to 5x faster inference and significantly higher throughput compared to general-purpose clouds.
• RDMA-ready Networking: Ensures minimal latency and stable performance across multi-node clusters, ideal for large-scale training and inference.
• Configuration Flexibility: Supports everything from serverless endpoints to root access and custom stacks on dedicated clusters.
• Multi-tenant Isolation: Ensures predictable and secure performance, preventing interference between different workloads on the same physical server or host.

GMI Cloud is aimed at development teams and companies that need to move AI prototypes into real-world production environments. It is especially valuable for real-time video generation applications, audio systems, and voice cloning, as well as for developing foundation models that require low inference latency and high resilience. By centralizing compute and orchestration on an optimized platform, users can efficiently scale their generative AI operations, reducing operational costs and accelerating technological iteration cycles.