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An AI-native inference cloud built for production AI, combining serverless scaling and dedicated GPU infrastructure with predictable performance and cost.

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Higgsfield
Reflection
Mirelo.ai
Pika
Utopai
HeyGen
Eigen AI
Radix Ark
Phinite AI
Cartesia
Singtel
Andromeda
Flow
Music.AI
LegalSign
IPSI
Higgsfield
Reflection
Mirelo.ai
Pika
Utopai
HeyGen
Eigen AI
Radix Ark
Phinite AI
Cartesia
Singtel
Andromeda
Flow
Music.AI
LegalSign
IPSI

Start serverless.
Scale for success

Run AI models instantly with serverless inference, then scale seamlessly into dedicated GPU infrastructure as your workloads grow.

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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 LomE

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

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Mirelo AI

Mirelo AI chose LomE 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

Higgsfield runs real-time generative video workloads on LomE 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

WiAdvance works with LomE 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.

AI inference infrastructure refers to the systems and compute resources used to run trained AI models in production. This includes GPUs, model serving frameworks, scaling systems, and networking designed to process real-time AI requests. Platforms like LomE provide infrastructure optimized for high-performance inference, enabling developers and companies to deploy LLMs, image models, video models, and other AI workloads reliably at scale.

Running AI inference at scale requires different infrastructure than traditional cloud workloads. AI models often need high-performance GPUs, optimized model serving engines, and efficient scheduling to reduce latency and cost. Dedicated inference infrastructure can provide better GPU utilization, predictable latency, and scalable deployment options compared with general-purpose cloud environments.

LomE supports a wide range of AI workloads including large language models (LLMs), image generation, video generation, audio models, and other multimodal AI systems. Teams can deploy open-source models or custom models and run them through serverless APIs, dedicated endpoints, or GPU clusters depending on performance and scaling requirements.

Moving from prototype to production typically requires infrastructure that supports reliable scaling, monitoring, and cost control. Developers often start with serverless inference APIs for experimentation and later transition to dedicated endpoints or GPU clusters for higher throughput and lower latency. Platforms like LomE allow teams to scale deployments without changing their application architecture.

AI inference cost can be optimized through efficient GPU utilization, batching strategies, and autoscaling infrastructure. By dynamically allocating GPU resources and scaling workloads based on traffic, teams can avoid paying for idle compute. Dedicated inference platforms also provide optimized model execution and resource scheduling to reduce overall cost compared with general-purpose cloud deployments.

Deploy models.
Run inference.
Scale automatically.

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