Synkti

ML inference on spot instances. 70-85% cost reduction through orchestration.

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Status

This project is actively being restructured. The codebase is transitioning to a cleaner workspace layout. Updates coming soon.

What works now:

• Simulation engine for validating scheduling policies
• Core types and traits for spot orchestration
• Agent scaffolding for spot instance monitoring

In progress:

• CLI for fleet management
• Full agent implementation
• Documentation

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What is Synkti?

Synkti orchestrates ML inference workloads on spot instances. Spot instances are 70-90% cheaper than on-demand, but can be terminated with only 2 minutes notice.

The key insight: you don't need complex checkpoint/restore mechanisms. GPU state can't be checkpointed anyway (CUDA contexts, VRAM). Instead, Synkti uses stateless failover - when a spot instance gets preempted, we drain in-flight requests and spawn a fresh container on a replacement instance.

Same cost savings, simpler architecture, actually works with GPUs.

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Repository Structure

crates/
├── synkti-core/        # Shared types and traits
├── synkti-agent/       # Runs on spot instances (monitoring, container lifecycle)
├── synkti-cli/         # Command-line interface
└── synkti-simulation/  # Policy testing and cost modeling

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Quick Start

git clone https://github.com/Bobby-Math/synkti.git
cd synkti/crates

# Run the simulation engine
cargo run -p synkti-simulation -- --duration 48 --tasks 100

# Build everything
cargo build --workspace

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License

AGPL-3.0

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Contact

Bobby - github.com/Bobby-Math