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Starts 7 June 2025 05:52
Ends 7 June 2025
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Overview
Explore MESA, an AI inference projection tool for evaluating LLM performance across different hardware and models, with detailed operation breakdowns and context length analysis.
Syllabus
- Introduction to LLM Inference Performance
- Introduction to MESA
- Setting Up MESA
- Evaluating LLM Performance
- Hardware Considerations
- Operation Breakdowns
- Context Length Analysis
- Case Studies
- Future Trends in LLM Inference
- Conclusion
Overview of large language models (LLMs)
Importance of performance projection for LLM applications
What is MESA?
Features and capabilities of MESA
Installation and configuration
Preparing the environment for inference analysis
Key metrics for performance evaluation
Comparison of different LLM architectures
Overview of hardware options for LLM inference
Analyzing performance across CPUs, GPUs, and TPUs
Understanding model operations
Breaking down inference processes in MESA
Impact of context length on inference performance
Techniques for optimizing context length in LLMs
Real-world examples of using MESA for performance evaluation
Lessons learned from different case studies
Emerging technologies and their impact on inference
Predictions for the future direction of LLM performance analysis
Summary of key learnings
Next steps in exploring and applying MESA for performance projection
Subjects
Computer Science
