Home

Scale Efficiently: Evaluate and Optimize Your LLM Deployments for Real-World Inference

GuideLLM is a platform for evaluating and optimizing the deployment of large language models (LLMs). By simulating real-world inference workloads, GuideLLM enables users to assess the performance, resource requirements, and cost implications of deploying LLMs on various hardware configurations. This approach ensures efficient, scalable, and cost-effective LLM inference serving while maintaining high service quality.

Key Features

• Performance Evaluation: Analyze LLM inference under different load scenarios to ensure your system meets your service level objectives (SLOs).
• Resource Optimization: Determine the most suitable hardware configurations for running your models effectively.
• Cost Estimation: Understand the financial impact of different deployment strategies and make informed decisions to minimize costs.
• Scalability Testing: Simulate scaling to handle large numbers of concurrent users without performance degradation.

Key Sections