LLMOps - Scaling LLM Deployment with ~100% Throughput Improvement

Executive Summary

Matics Analytics successfully optimized and scaled the deployment of LLMs (OpenAI's Whisper model) for Ecosmob Technologies, significantly improving performance and resource utilization.

The project focused on leveraging GPU resources effectively, enhancing throughput and creating a flexible configuration system for future scalability.

Challenges

Ecosmob faced limitations in their existing Whisper model deployment:

1. Under-utilization of available GPU resources

2. Limited throughput due to inefficient worker allocation

3. Inflexibility in scalability management, hindering adaptability to infrastructure changes

Optimized Solution

Our team implemented a multi-faceted architecture to address these challenges:

1. Parallel Distribution on GPUs

-> Objective: Fully utilize the available GPU resources in the cluster.

-> Outcome: All available GPUs were engaged, distributing the workload more efficiently across the hardware resources.

2. Enhanced Throughput

-> Objective: Mitigate the GPU core bottleneck and enhance throughput.

-> Outcome: Enabled each pod to handle more concurrent tasks, improving the overall processing rate.

3. Dynamic Configuration Management

-> Objective: Allow easy adjustments to configuration settings without rebuilding the application image.

-> Outcome: Made the system more adaptable to changes in infrastructure, allowing performance tuning based on the current environment.

Results

The optimization efforts led to significant performance improvements:

Key Achievement: Processing time decreased from approximately 22.46 to 11.23, representing an average improvement of 100% in throughput.

LLM Scalability and Future Considerations

To ensure future scalability, we provided detailed capacity planning guidelines for adjusting the deployment based on infrastructure changes:

Built a comprehensive capacity planning guide, including:

- Performance benchmarks results for NVIDIA Tesla P100, A100 and T4 GPUs

- System architecture considerations for auto scaling 

- Formulas for estimating resource requirements under dynamic loads

- Server form factor recommendations for different scales of deployment

Value Delivered

The optimizations implemented in this project delivered significant value to Ecosmob:

Doubled Processing Capacity:

• The average 100% improvement in throughput allows to process roughly twice as much audio data in the same amount of time, significantly increasing their operational efficiency.

Cost Efficiency

• By fully utilizing available GPU resources, we've maximized the return on investment (ROI) for client's existing hardware infrastructure.

Scalability

• The implementation of dynamic configuration management enables to easily scale their system as demand grows, without requiring extensive redevelopment.

• The more efficient use of GPU cores and workers per pod ensures that we're getting the most out of their computing resources, potentially delaying the need for additional hardware investments.

Detailed Planning for Future Growth

• The scalable architecture and detailed capacity planning guide provide Ecosmob with a clear roadmap for future expansions, allowing them to confidently plan for increased demand.

A few words of appreciation

Tech Stack

• OpenAI

• Hugging Face

• PyTorch

• TorchServe

• FastAPI

• Kubeflow

Conclusion

With LLMOps, we successfully optimized Whisper model deployment, achieving significant performance improvements and establishing a framework for future scalability.

By leveraging GPU resources effectively and implementing dynamic configuration management, we've created a robust, adaptable system capable of handling increased workloads efficiently.

As Ecosmob's needs grow, the flexible architecture and detailed capacity planning guide will enable smooth scaling of their audio transcription capabilities.