OctoML

Overview

OctoML is a platform for optimizing, packaging, and deploying machine learning models efficiently across diverse hardware targets. We use OctoML to accelerate inference performance, reduce serving costs, and simplify the path from trained model to production deployment without deep hardware expertise.

Our Capabilities

• Automated model optimization for CPU, GPU & accelerators
• Cross-framework model compilation (TVM-based)
• Container-based model packaging for any cloud
• Benchmarking across hardware targets
• Latency & throughput optimization
• One-click deployment to cloud endpoints
• Support for PyTorch, TensorFlow, ONNX models
• Cost-performance tradeoff analysis

Common Use Cases

• Reducing inference costs in production
• Deploying models on edge hardware
• Multi-cloud model serving strategies
• Optimizing LLM serving throughput