AI infrastructure. Built for scale.
Deploy LLMs, train models, and run inference workloads across distributed GPU infrastructure with automatic scaling and cost optimization.
“Blazing simplified our AI infrastructure. We went from weeks of setup to deploying LLMs in minutes.”
Sergio Charrua
Founder, Kahea AI
Deploy an LLM in minutes
Blazing Core handles GPU provisioning, model serving, and auto-scaling automatically.
Built for production workloads
GPU Orchestration
Automatic GPU provisioning and management across multiple cloud providers with intelligent workload placement.
- Multi-GPU support
- Automatic failover
- GPU utilization tracking
- Cost-optimized placement
Model Serving
Deploy models as scalable API endpoints with automatic batching, caching, and load balancing.
- Auto-scaling inference
- Model versioning
- A/B testing support
- Sub-100ms latency
Training Pipelines
Distributed training with automatic checkpointing, experiment tracking, and resource optimization.
- Distributed training
- Automatic checkpointing
- Experiment tracking
- Spot instance support
Faster Deployment
Cost Reduction
Inference Latency
Uptime SLA
Everything you need
Model Registry
Version and manage models with built-in artifact storage and metadata tracking.
Cost Optimization
Reduce GPU costs by 60% through spot instances and intelligent workload scheduling.
Monitoring & Observability
Real-time GPU metrics, model performance tracking, and detailed usage analytics.
Multi-Framework Support
Native support for PyTorch, TensorFlow, JAX, and popular inference engines.
Ready to get started?
Join leading teams building production infrastructure with Blazing