ScaleAI-Inference-Orchestrator is a specialized framework designed to handle massive-scale AI inference requests. It bridges the gap between raw ML models and production-grade distributed systems, ensuring that GenAI workloads are served with minimal latency and maximal hardware utilization.
- Latency-Aware Request Routing: Dynamically routes inference tasks to the most optimal worker nodes based on real-time hardware telemetry.
- Intelligent Dynamic Batching: Aggregates individual inference requests into optimized batches to saturate GPU/TPU pipelines effectively.
- Production-Ready Observability: Integrated with structured logging and performance tracking for sub-millisecond request auditing.
- Modular Model Gateway: Decouples API consumers from backend model providers (OpenAI, Anthropic, or Local LLMs).
mermaid graph TD A[Client Request] --> B{Inference Orchestrator} B -->|Check Load| C[Worker Node A - GPU] B -->|Check Load| D[Worker Node B - TPU] C --> E[Model Registry] D --> E B --> F[Monitoring & Telemetry]
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Clone the Repo
οΏ½ash git clone https://github.com/gauravkukal/ScaleAI-Inference-Orchestrator.git cd ScaleAI-Inference-Orchestrator -
Install Dependencies
οΏ½ash pip install -r requirements.txt -
Run Orchestrator Service
οΏ½ash python main.py
Conceptualized and architected by Gaurav Kukal, Sr. Director of Engineering at Adobe. Focused on building high-performing, innovation-driven AI foundations.
Driven by daily clarity and measurable business-focused delivery.