Granite 4.0 Micro (non-reasoning)

Since you're not paying per token, your primary cost driver is the compute resources (GPUs) required to run the model. Efficient infrastructure management is key.

• **Right-size Instances:** Choose GPU instances that match your inference workload. Avoid over-provisioning for sporadic tasks, but ensure enough power for peak loads.
• **Auto-Scaling:** Implement auto-scaling groups to dynamically adjust resources based on demand, spinning up instances during high traffic and scaling down during low periods.
• **Spot Instances/Preemptible VMs:** For non-critical or batch processing tasks, utilize cheaper spot instances or preemptible VMs to significantly reduce compute costs.
• **Containerization:** Package your model and inference stack in containers (e.g., Docker) for consistent deployment across different environments and easier resource management.

To get the most out of your GPU resources, aim to process multiple requests simultaneously rather than one by one.

• **Dynamic Batching:** Implement dynamic batching where multiple incoming requests are grouped together and processed in a single inference pass, maximizing GPU utilization.
• **Asynchronous Processing:** For tasks that don't require immediate responses, process requests asynchronously to allow for optimal batching and resource scheduling.
• **Queue Management:** Use message queues (e.g., Kafka, RabbitMQ) to buffer incoming requests, enabling your inference service to pull and batch them efficiently.
• **Model Optimization:** Explore techniques like quantization or ONNX runtime to further optimize the model for faster inference on your chosen hardware.

Different applications have different performance and cost requirements. Tailor your deployment strategy accordingly.

• **Edge Deployment:** For very low-latency or privacy-sensitive applications, consider deploying the model on edge devices, reducing cloud egress costs and improving response times.
• **Serverless Inference:** Explore serverless options (e.g., AWS Lambda with GPU support, Google Cloud Run) for intermittent or event-driven workloads, paying only for actual execution time.
• **Hybrid Cloud:** Combine on-premises infrastructure for stable base loads with cloud bursting for peak demands, optimizing capital expenditure and operational flexibility.
• **Caching Mechanisms:** Implement caching for frequently requested or identical outputs to reduce redundant inference calls and save compute cycles.

Even with zero token costs, continuous monitoring is essential to prevent unexpected infrastructure expenses.

• **Detailed Cost Tracking:** Utilize cloud provider cost management tools to track GPU usage, storage, and network transfer costs associated with your model deployment.
• **Performance Metrics:** Monitor GPU utilization, memory usage, and inference latency to identify bottlenecks and opportunities for optimization.
• **Alerting:** Set up alerts for unusual spikes in resource consumption or costs to proactively address potential issues.
• **Regular Review:** Periodically review your deployment architecture and resource allocation to ensure it remains aligned with your application's evolving needs and budget.