Grid
Distributed computing components and utilities for scalable AI workloads
Core Components
Distributed Processing
Scale AI workloads across multiple nodes with automatic load balancing and fault tolerance.
- • Auto-scaling compute clusters
- • Task queue management
- • Resource optimization
- • Failure recovery
Parallel AI Training
Distribute model training across GPUs and nodes for faster convergence and larger models.
- • Multi-GPU training
- • Model parallelism
- • Gradient synchronization
- • Checkpoint management
Data Pipeline
Process large datasets efficiently with distributed data loading and preprocessing.
- • Streaming data ingestion
- • Parallel preprocessing
- • Data validation
- • Format conversion
Inference Scaling
Deploy and scale AI models for high-throughput inference with automatic optimization.
- • Model serving clusters
- • Request batching
- • A/B testing
- • Performance monitoring
Architecture
Cloud-Native Design
Built for Kubernetes with containerized components that scale automatically based on workload demands.
- Kubernetes-native deployment
- Horizontal pod autoscaling
- Multi-cloud compatibility
- Resource monitoring & alerts
Deployment Example
apiVersion: grid.hybrid.ai/v1
kind: ComputeCluster
spec:
replicas: 3-10
resources:
gpu: nvidia-a100
memory: 32Gi
workload:
type: training
model: llama-7bUse Cases
Large Model Training
Train foundation models like GPT, BERT, or custom architectures across multiple GPUs and nodes.
Batch Processing
Process large datasets for ETL, feature engineering, or model inference at scale.
Real-time Inference
Serve AI models with low latency and high availability for production applications.
Scale Your AI Workloads
Deploy Grid to accelerate your AI training and inference pipelines