llm-benchmark

Benchmarking local Ollama models requires running automated scripts that execute inference across multiple contexts, measure latency and throughput, and generate Markdown or JSON reports for performance comparison.

Comparing local LLM throughput and VRAM consumption involves running a benchmark across multiple context sizes, which tracks memory management and latency to identify the optimal hardware configuration.

Running local LLM inference benchmarks requires Ollama installed in your developer environment to pull models and execute the script-driven benchmarking process.

Automated GPU memory management during local LLM benchmarking controls VRAM allocation while pulling models and executing inference, ensuring stable performance measurement across different context configurations.

Generating Markdown and JSON reports for tracking local LLM model performance is supported natively by the benchmarking process, providing structured outputs for later comparison and review.

Limitations of script-driven benchmarking include dependency on local hardware configurations and the Ollama environment, meaning results may not generalize to different GPU setups or cloud deployments.