Realistic Benchmarking for LLMs: Context, Multimodal, Hardware, Parallel
Benchmark LLMs with realistic context sizes, multimodal workloads, hardware specs, and parallel processing to reflect real-world performance.
Apply these guidelines when creating new benchmark tests to ensure realistic performance data.
Summary
Benchmarking LLMs requires realistic workloads that reflect real‑world usage. The author emphasizes that context size is critical; long sessions or larger contexts should be used to simulate agentic, coding, or RAG tasks. Multimodal models should be benchmarked with image processing tasks to capture their true performance. Benchmarkers should explicitly state their hardware configuration, as GPU variants can significantly affect results. Parallel processing benchmarks are also essential, especially for agentic workloads that involve multiple concurrent requests. Following these guidelines will produce more useful performance data for the community.
Key changes
- Context size matters; use long sessions or larger context for realistic benchmarks.
- Multimodal models should be benchmarked with image processing tasks.
- Specify hardware configuration, as GPU variants differ.
- Include parallel processing benchmarks for agentic workloads.