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[content]
AI-Native Inference Cloud Powered by NVIDIA — GMI Cloud
Inference
Compute
Customers
Developers
Pricing
Company
English
Sign In
Contact Sales
Powered by NVIDIA
One cloud for
|
compute, inference,
compute, inference,
and agents.
and agents.
AI infrastructure built for production scale — GPU clusters, optimized inference, and agent runtime on one unified cloud.
AI infrastructure built for production scale — GPU clusters, optimized inference, and agent runtime on one unified cloud.
Start in Console
Contact Sales
Start serverless
.
Scale for success
Run AI models instantly with serverless inference, then scale seamlessly into dedicated GPU infrastructure as your workloads grow.
Start in Console
Automatic scaling to zero with no idle cost
Built-in batching and latency-aware scheduling
Production-ready APIs for LLM and multimodal models
Multi-tenant isolation for predictable performance
Automatic scaling to zero with no idle cost
Built-in batching and latency-aware scheduling
Production-ready APIs for LLM and multimodal models
Multi-tenant isolation for predictable performance
When serverless isn't enough, Take Control.
When serverless isn't enough, Take Control.
Built on NVIDIA Reference Platform Cloud Architecture and validated designs for performance, reliability, and scale.
Explore GPU Infrastructure
Dedicated bare metal GPUs with predictable performance.
Our Cluster engine orchestrates multi-node cluster at the infrastructure layer.
Root access and custom stacks when infrastructure matters.
GPU Pricing
Transparent GPU pricing for production AI workloads across NVIDIA H100, H200, and Blackwell platforms.
View GPU Pricing
NVIDIA H100
$2.00
/GPU-hour
Ideal for inference and training jobs needing high memory bandwidth and larger model footprints.
AVAILABLE NOW
NVIDIA H200
$2.60
/GPU-hour
Optimized for training and inference at scale with strong performance, availability, and ecosystem support.
AVAILABLE NOW
NVIDIA Blackwell
Pre-order
Best for teams planning large-scale deployments that require maximum performance headroom.
coming soon
Production AI Runs Better on GMI Cloud
Real performance gains across production AI workloads.
3.7x
Higher throughput
5.1x
Faster inference
30%
Lower cost
2.3x
Faster Scaling When Demand Spikes
Based on real production inference traffic, including real-time and batch workloads, using equivalent model configurations.
Inference-First by Design
Inference is serverless by default. Scaling, traffic handling, and cost optimization happen automatically, including scaling to zero.
Serverless by Default
Inference runs serverless by default, with automatic scaling, request batching, and cost-aware scheduling.
Performance at Scale
Dedicated GPU clusters with RDMA-ready networking ensure stable throughput under sustained load.
Flexible by Design
Scale from API-based inference to full GPU clusters without re-architecting your stack.
Trusted by Leading AI Teams
Mirelo AI chose GMI Cloud as its AI infrastructure partner to scale foundational model development with lower cost, faster iteration, and startup-friendly flexibility.
40% lower training costs
20% faster training time
10–15% lower infrastructure cost vs. alternatives
Flexible commercial structure tailored to startup needs
Higgsfield runs real-time generative video workloads on GMI Cloud with lower latency, lower compute cost, and production-grade reliability.
65% lower p95 inference latency
45% lower compute cost
99.9% request success rate under peak traffic
Production-grade endpoint resilience
WiAdvance works with GMI Cloud to support public-sector and enterprise AI adoption in Taiwan through flexible infrastructure allocation and managed AI access.
Trusted SI / channel-led delivery model
Supports government and education-related use cases
Flexible allocation across committed and on-demand capacity
Detailed usage reporting for downstream operations
FAQ
Get quick answers to common queries in our FAQs.
What is AI inference infrastructure?
Why do companies need specialized infrastructure for AI inference?
What types of AI workloads can run on GMI Cloud?
How do teams move from AI prototype to production deployment?
How can companies reduce the cost of large-scale AI inference?
Deploy models. Run inference. Scale automatically.
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Run inference
.
Scale automatically.
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