Inference Engine by GMI Cloud
AIA scalable, GPU-optimized inference serving solution and cloud platform for deploying high-performance AI models.
-(0 Reviews)
Paid
See pricing details
Inference Engine by GMI Cloud
AI
A scalable, GPU-optimized inference serving solution and cloud platform for deploying high-performance AI models.
-(0 Reviews)
Paid
See pricing details
About Inference Engine by GMI Cloud
Inference Engine by GMI Cloud is a GPU cloud and inference-serving solution for deploying, scaling, and operating machine learning models. It combines high-performance GPU infrastructure with orchestration and SDK tooling to provide low-latency, datacenter-scale model serving. The platform targets both real-time and batch inference workloads and integrates with Kubernetes-native deployment patterns and developer SDKs to streamline MLOps and production deployment.
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Key Features
Datacenter-Scale Serving: A distributed inference serving framework designed to run across multi-node GPU clusters for horizontal scaling and low-latency model responses.
GPU-Optimized Infrastructure: Provides access to high-performance GPU instances and configurations tuned for deep learning inference to maximize throughput and reduce latency.
Kubernetes-Native Orchestration: Integrates with Kubernetes deployment patterns to enable containerized model deployments, autoscaling, and cluster-aware scheduling.
Developer SDKs and APIs: SDKs (including a Python SDK) and APIs for programmatic model deployment, versioning, and invoking inference endpoints from applications and pipelines.
Multi-Workload Support: Supports both real-time (low-latency) and batch inference workloads, allowing users to run large models interactively or process bulk jobs.
Model Management & Versioning: Tools and workflows for registering, versioning, and routing traffic to specific model versions to support safe rollouts and A/B testing.
Datacenter-scale distributed inference serving framework (Rust) for high-throughput model serving
Python SDK available (public GitHub repository) for integration and API access
GPU-optimized cloud infrastructure for AI training, inference, and deployment
Designed for scalable, production-grade model deployment across GPU instances
Public GitHub presence with multiple repositories and an official support contact
Use Cases
Low-Latency LLM Serving: Host large language models behind HTTP/gRPC endpoints for chatbots and conversational agents requiring sub-second responses.
Scaling Vision Inference: Deploy computer vision models across a GPU cluster to handle high-throughput image or video inference pipelines.
Batch Prediction Jobs: Run large-scale batch inference for analytics and offline scoring using GPU-accelerated batch workers.
MLOps Integration: Integrate with CI/CD and Kubernetes-based MLOps pipelines to automate model deployments, rollbacks, and canary releases.
Multi-Cloud & Hybrid Deployments: Operate model serving across on-premise and cloud GPU resources to meet data locality, compliance, or cost requirements.
Production Model Rollouts: Use model versioning and traffic routing to perform safe production rollouts and A/B tests of model updates.
Serving deep learning models at scale on GPU clusters
Production model inference for latency-sensitive applications
Deploying and managing large-model inference workloads in the cloud or datacenter
Integration into ML pipelines via Python SDK for automated inference workflows
