- Performance Profiling: Advanced evaluation of end-to-end performance to identify computational bottlenecks and detect architectural limitations.
- Scalability Benchmarking: Comprehensive testing on HPC infrastructure to provide a complete overview of scaling properties for both training and fine-tuning.
- Model Optimization: Technical support for implementing efficiency techniques, such as pruning and quantization, to define optimal configurations for deployment.
Key Value: Enabling organizations to estimate resource requirements, like GPU hours, and identify the highest-performing models achievable within a fixed compute budget.
Practical example
Bridging the gap between a local prototype and high-traffic production environments is critical.
This service facilitates, among other activities:
- Resource Estimation: Helping companies pre-training or fine-tuning models on custom data to accurately estimate required GPU hours and infrastructure scaling.
- Inference Dimensioning: Profiling workloads for vLLM-based endpoints to determine the required GPU setup, parallelism, and batching strategies.
- Load Analysis: Identifying expected latency and throughput under load to ensure service stability for citizen-facing or enterprise applications.
Case Study: A startup builds a citizen-facing chatbot on an open-source LLM. The service profiles the workload and identifies the necessary GPU setup and batching strategy, which ensures the startup supports production traffic levels with optimized latency.
