flashtensors focuses on minimizing model cold-start times from disk to GPU, enabling rapid on-device inference and model hot-swapping.
Overview
flashtensors is a high-performance model loading and inference support library designed to reduce cold-start times to seconds and allow hot-swapping of large models on a single device. It offers a daemon, CLI, and Python SDK, integrates with backends like vLLM, and provides tools to register and load models in a fast-loading format exposed via gRPC or local APIs.
Key Features
- Ultra-fast loading: significant speed improvements over traditional loaders, with cold-starts typically measured in low seconds.
- Hot-swap and multi-model residency: host and switch between dozens to hundreds of models on the same device.
- Daemon + CLI: commands such as
flash start,flash pull, andflash runsupport operational workflows. - SDK integrations: Python SDK to integrate with inference backends (vLLM, etc.).
Use Cases
- Edge and on-prem inference: robotics, wearables, and private deployments where low latency is critical.
- Multi-model services: platforms that need to manage many models on shared hardware.
- Serverless inference: reduce resource usage and cold-start costs by loading models on demand.
Technical Details
- Efficient serialization and chunked loading reduce I/O and GPU preparation time.
- Configurable memory pools and threading for hardware-specific optimization.
- Built-in benchmarks and tooling to measure load time and memory footprint for capacity planning.