Serverless inference
Run inference with automatic scaling to zero, built-in batching, latency-aware scheduling, and production-ready APIs for LLM and multimodal models.
GMI Cloud
RevendiquerGMI Cloud is an AI infrastructure platform for production inference, training, and fine-tuning on NVIDIA GPUs with serverless inference, dedicated clusters, and bare metal.
Overview
GMI Cloud is an AI-native inference cloud built for production AI workloads on NVIDIA infrastructure. It combines serverless inference, dedicated GPU clusters, and bare metal GPU deployments on one platform so teams can start with managed inference and move into deeper infrastructure control as their needs grow.
The platform is positioned for inference, training, fine-tuning, and large-scale GPU workloads. The source emphasizes predictable performance, automatic scaling, and dedicated resources rather than shared environments, with pricing published for specific NVIDIA GPU classes and capacity options.
Core capabilities
Flexible deployment paths
Move from API-based inference to dedicated GPU clusters without changing platforms, using the same infrastructure foundation across deployment modes.
Dedicated GPU infrastructure
Use dedicated NVIDIA GPUs inside GMI-operated data centers for sustained workloads that need predictable performance and isolated resources.
Bare metal control
Deploy bare metal servers with full root access and custom stacks when infrastructure control matters more than managed abstractions.
Cluster networking
Run multi-node GPU clusters with RDMA-ready networking for workloads that need stable throughput under sustained load.
Broad NVIDIA hardware coverage
Choose on-demand, reserved, or pre-order capacity across NVIDIA H100, H200, Blackwell, GB200, GB300, and B200 offerings.
Common use cases
Production model serving
Start with managed inference for LLM or multimodal models, then keep the same platform as traffic grows and you need more control or capacity.
Model training and fine-tuning
Run long-lived or high-utilization training and fine-tuning jobs on dedicated NVIDIA GPUs with predictable performance and isolated resources.
Distributed cluster workloads
Use multi-node GPU clusters with RDMA-ready networking for distributed workloads that need stable throughput at scale.
Infrastructure-controlled deployments
Deploy bare metal servers and custom stacks when you need root access, hardware-level control, or a specific infrastructure setup.
Capacity planning and scaling
Choose on-demand or reserved GPU capacity to match short-term experiments, elastic growth, or more predictable long-term usage.
Pros and Cons
Pros
- Supports both serverless inference and dedicated GPU infrastructure on the same platform.
- Publishes GPU pricing for several NVIDIA classes, including H100, H200, B200, GB200, and GB300.
- Offers deployment options from container service to bare metal and managed multi-node clusters.
- Describes automatic scaling, batching, and latency-aware scheduling for inference workloads.
- Includes dedicated resources and isolation rather than shared GPU environments.
Cons
- The source does not provide detailed integration, API, or framework documentation.
- Some newer GPU options are listed as pre-order, limited availability, or contact sales only.
- Pricing details for networking, storage, and full system bundles are not spelled out on the pages provided.
FAQ
What is GMI Cloud used for?
GMI Cloud provides production AI infrastructure for serverless inference, dedicated GPU clusters, and bare metal GPU deployments on NVIDIA hardware.
How does GMI Cloud handle scaling?
The source describes serverless inference by default, with scaling to dedicated GPU infrastructure when workloads grow or need more control.
Which NVIDIA GPUs are available on the platform?
The pricing page lists NVIDIA H100, H200, Blackwell, GB200, GB300, and B200 options, with some GPUs available now and others listed as pre-order or contact sales.
How is pricing structured?
The source indicates dedicated resources, on-demand and reserved capacity options, and no hidden fees, but it does not provide a full breakdown of network or storage charges.
Quick Facts
- Category
- AI infrastructure
- Primary use
- Production inference and GPU workloads
- Platform
- NVIDIA GPU cloud
- Deployment modes
- Serverless inference, dedicated GPU clusters, bare metal
- Pricing model
- Published GPU hourly pricing plus reserved and on-demand capacity
- Domain
- gmicloud.ai