Principle:Ggml org Llama cpp Performance Benchmarking
| Knowledge Sources | |
|---|---|
| Domains | Benchmarking, Performance |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Performance Benchmarking is the principle of systematically measuring and comparing inference speed, throughput, and computational efficiency across configurations.
Description
This principle covers the benchmarking infrastructure used to evaluate llama.cpp performance across different models, quantization types, hardware configurations, and batch sizes. It includes comprehensive benchmarking tools for end-to-end inference (llama-bench), batched throughput measurement, low-level dot product microbenchmarks (vdot, q8dot), idle overhead measurement, and storage of benchmark results for competitive evaluations like AIME25.
Usage
Apply this principle when evaluating performance regressions, comparing quantization methods, optimizing for specific hardware, or producing benchmark results for publication and comparison.
Theoretical Basis
Performance benchmarking in LLM inference measures several key metrics: prompt processing speed (tokens per second for the initial prompt evaluation), generation speed (tokens per second for autoregressive generation), time to first token (latency), and peak memory usage. These metrics are influenced by model size, quantization type, batch size, number of GPU layers, thread count, and hardware capabilities. Microbenchmarks isolate specific computational kernels (such as quantized dot products) to identify bottlenecks. The benchmarking framework controls for warmup effects, measures statistical variance, and produces structured output for comparison across runs.
Related Pages
- Implementation:Ggml_org_Llama_cpp_Llama_Bench
- Implementation:Ggml_org_Llama_cpp_Batched_Bench
- Implementation:Ggml_org_Llama_cpp_Vdot_Benchmark
- Implementation:Ggml_org_Llama_cpp_Q8dot_Benchmark
- Implementation:Ggml_org_Llama_cpp_Idle_Benchmark
- Implementation:Ggml_org_Llama_cpp_AIME25_Benchmark_Results