A Unified AI Inference Platform

Run any model in production with predictable latency, cost, and reliability.

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Model-as-a-ServiceDedicated EndpointsServerless APIs

One Inference Engine.
Multiple Execution Modes.

It supports LLM, image, video, and multimodal inference through a single, consistent platform.

Unified Runtime

Single execution layer for LLM, image, video, audio, and multimodal inference.

Scalable Orchestration

Built-in batching, scheduling, and scaling across GPU clusters.

API Control

Self-serve APIs with predictable latency, usage control, and deployment flexibility.

Models Running in Production

Browse production-ready models optimized for latency, throughput, and operational stability.

Gemini 3.1 Flash

Google

LLM

Gemini 3 Pro

Google

LLM

GPT-5.5

OpenAI

LLM

GLM-5.1 FP8

Zhipu

LLM

Owen3.5 Max Preview

Alibaba

LLM

Claude Sonnet

Anthropic

LLM

Qwen 3

Alibaba

LLM

Flexible Inference Deployment Options

Use the same inference engine across multiple execution modes, from instant serverless APIs to dedicated GPU endpoints and fine-tuned models.

Model-as-a-Service (MaaS)

Instant access to experimentation, prototyping and production-ready models via unified API, ideal for rapid integration and cost-efficient inference.

Explore MaaS

Fine-Tuning

Tailor an AI for your use-case. Train base models with your own data, then deploy them using the same platform. Improve output quality and behavior while keeping a consistent serving and usage experience.

Explore Studio

Serverless Dedicated Endpoints

Start with serverless public APIs for instant scaling and pay-as-you-go usage. Upgrade to dedicated endpoints for workload isolation, stable latency, and predictable performance.

Trusted by Leading AI Teams

View Customers

Eigen AI uses LomE for flexible model access across production endpoints and third-party APIs, supporting both customer-facing serving and evaluation workloads.

Uses MaaS with Gemini and Anthropic APIs
Dedicated endpoints for production workloads
Supports both production and benchmarking use cases
Flexible infrastructure mix across VMs and CPU nodes

WiAdvance uses LomE to deliver ready-to-use model access across Gemini, Claude, and GPT for downstream enterprise and public-sector use cases.

Endpoint-based access to Gemini, Claude, and GPT
Simplifies AI adoption for downstream customers
Supports channel-led enterprise delivery
Managed model access without raw infrastructure overhead

LegalSign uses managed model access on LomE to power legal automation workflows with faster document processing and lower manual effort.

Supports legal workflow automation
Accelerates document review and compliance tasks
Reduces operational friction for AI adoption
Managed model access for business users

FAQ

Get quick answers to common queries in our FAQs.

An AI inference engine is the runtime system responsible for executing trained models and generating outputs from user inputs. It handles tasks such as model loading, request processing, GPU scheduling, and response generation. Inference engines are designed to deliver low-latency responses while efficiently utilizing GPU resources for large-scale AI workloads.

Developers typically deploy AI models through APIs provided by an inference platform. After selecting a model, they can access it via REST or SDK-based APIs to process requests such as text prompts, images, or audio inputs. Inference platforms manage scaling, GPU allocation, and request routing behind the scenes.

LomE supports a wide range of production-ready AI models including open-source and proprietary models. This includes large language models, image generation models, video models, and multimodal systems. Developers can explore available models in the model library and deploy them through a consistent API interface.

Serverless inference allows developers to run AI models without managing infrastructure. The platform automatically allocates GPU resources and scales based on demand. Dedicated endpoints provide reserved compute resources for consistent performance, making them suitable for production workloads with predictable traffic or strict latency requirements.

Inference engines optimize performance through GPU scheduling, efficient model execution, and distributed request handling. By running models closer to users and optimizing GPU utilization, inference platforms can significantly reduce response time compared with general-purpose cloud deployments.

How Will You Deploy Your Models?

Start running models instantly or configure dedicated GPU endpoints for production workloads.

Start in Console Explore GPU Infrastructure