68% Reduction in GenAI Run-cost and Improved Performance in Just One Week

A client was an early-adopter of GenAI, and had developed and deployed a custom large language model (LLM) in their Databricks production environment. This model was designed to extract key information from document sets and pass structured outputs to a downstream database. However, as usage scaled, the client faced two critical challenges: rising operational costs and high latency, which prevented them from meeting their service level agreements (SLAs). The primary cost drivers were model provisioning and inference serving, and the high response times were impacting business-critical workflows.

Recognizing the need for expert intervention, the client engaged Aimpoint to diagnose inefficiencies and implement optimizations that would improve both cost-effectiveness and performance.

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The Aimpoint team collaborated closely with the client’s engineering team to conduct an in-depth evaluation of their LLM model deployment. This included reviewing their model architecture, inference pipeline, and Databricks configuration. By benchmarking the implementation against industry best practices, we identified key areas for both cost and speed optimization. Aimpoint identified the primary cost drivers and focused on optimizing the model to address these areas. As potential improvements were identified, Aimpoint collaborated closely with the client to implement these changes and validate their effectiveness.

Key optimizations

Reduce LLM operating cost

• Reduced footprint of the LLM in their training environment to lower VRAM usage and reduce hosting costs
• Recommended disabling gradient tracking and leveraging batch processing to optimize resource consumption.
• Integrated new libraries for more efficient inference to reduce latency.
• Explored the feasibility of deploying smaller, more efficient models capable of running on CPUs, significantly lowering hardware costs.

Enhance Performance and Reduce Latency

• Advised on implementing techniques that balanced speed improvements with memory efficiency increases.
• Provided best practices for Kubernetes-based autoscaling and resource monitoring to ensure compute resources were dynamically allocated based on demand.
• Optimized inference scheduling and pipeline orchestration to reduce unnecessary processing delays.

Improve Deployment Scalability and Maintainability

• Recommended advanced monitoring strategies to track GPU/CPU utilization and improve workload management.
• Designed an infrastructure scaling approach tailored to the client’s usage patterns, preventing over-provisioning while ensuring SLAs were met.

By implementing these recommendations, the client achieved substantial cost reductions without sacrificing model performance and significantly improved response times, ensuring AI-driven workflows remained efficient and scalable.