This case study explores how Moveworks, an enterprise AI company, optimized their production Copilot system using NVIDIA's TensorRT-LLM engine to achieve significant improvements in latency and throughput. The study provides valuable insights into the challenges and solutions of deploying LLMs in production environments where real-time performance is critical. The core challenge Moveworks faced was maintaining responsive, natural conversations in their enterprise Copilot while scaling to handle multiple concurrent users. Even small delays in response time can disrupt the conversational flow and user experience. This is a common challenge in production LLM deployments where balancing performance with resource utilization is crucial. Technical Implementation and Optimizations: The implementation leveraged several key technical optimizations provided by TensorRT-LLM: * Multi-block mode with Flash-Decoding: This optimization breaks down attention operations into smaller parallel tasks, significantly reducing memory usage and processing time for longer conversations. This is particularly important in production where conversation history needs to be maintained while keeping response times low. * SmoothQuant: This quantization technique optimizes model size by converting weights and activations to smaller formats without significant accuracy loss. The reduced model size leads to faster inference times and better resource utilization in production. * Memory and Computation Optimizations: * Int8_kv_cache for efficient storage of intermediate results * Custom CUDA kernels through gpt_attention_plugin for optimized attention operations * Specialized gemm_plugin for accelerated matrix multiplication The results demonstrated significant improvements in several key metrics: * Token Processing Speed: Increased from 19 to 44 tokens per second (2.32x improvement) * Average Request Latency: Reduced from 3401ms to 1446ms (2.35x improvement) * Time to First Token: Decreased from 789ms to 292ms (2.7x improvement) Production Deployment Considerations: The case study highlights several important aspects of successful LLM deployment in production: 1. Resource Optimization: The improvements allowed Moveworks to handle more concurrent conversations on existing infrastructure, avoiding costly horizontal scaling. This is crucial for production environments where infrastructure costs can quickly escalate with user load. 2. User Experience Focus: The optimization strategy prioritized metrics that directly impact user experience, particularly the time to first token. This allows for streaming responses that feel more natural and responsive, even before the full response is generated. 3. Hardware Utilization: The implementation takes advantage of GPU-specific optimizations through NVIDIA's technology stack, showing the importance of hardware-software alignment in production LLM deployments. 4. Scalability: The optimizations enable better handling of demand surges without performance degradation, a critical requirement for enterprise-grade applications. Technical Infrastructure and Integration: The implementation required careful integration between Moveworks' existing infrastructure and NVIDIA's tools. The flexible Python API provided by TensorRT-LLM allowed for customization of optimizations while maintaining compatibility with existing models. This highlights the importance of choosing tools that offer both performance improvements and integration flexibility in production environments. Performance Monitoring and Optimization Strategy: The case study demonstrates a systematic approach to performance optimization: * Baseline Measurement: Clear documentation of pre-optimization performance metrics * Targeted Optimizations: Focus on specific aspects of the model pipeline that impact user experience * Comprehensive Metrics: Tracking multiple performance indicators beyond just raw speed * Production-Relevant Benchmarks: Testing under conditions that mirror real-world usage Future Considerations and Scalability: Moveworks' implementation shows forward-thinking considerations for production LLM deployment: * The optimizations provide headroom for handling future increases in user load * The infrastructure can support more sophisticated models without requiring proportional hardware scaling * The approach maintains flexibility for incorporating future optimizations and improvements Lessons for LLMOps Practitioners: This case study offers several valuable insights for others deploying LLMs in production: * The importance of measuring and optimizing for the right metrics that impact user experience * The value of specialized optimization tools in achieving production-grade performance * The need to balance multiple factors including latency, throughput, and resource utilization * The benefits of hardware-specific optimizations when deploying at scale The case study demonstrates that successful LLM deployment in production requires careful attention to optimization at multiple levels - from model-level optimizations to hardware utilization. The results show that significant performance improvements are possible while maintaining model capability, making advanced LLM applications viable for enterprise use cases where performance and resource efficiency are crucial.