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hardware-profiling

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Jasmin0407 / benchmark-pipeline-public

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A reproducible benchmarking pipeline for machine learning models, focused on analyzing inference efficiency. It captures CPU utilization, MACs, memory consumption (RSS), model size, and runtime-specific resource demands, combined with hardware-aware profiling for consistent cross-system evaluation.

benchmarking machine-learning inference pytorch performance-analysis cpu-usage macs onnx ml-models resource-monitoring hardware-profiling
  • Updated Mar 9, 2026
  • Python

TJ-Neary / AI_Eval

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Comprehensive LLM evaluation framework comparing local and cloud models with hardware-aware benchmarking. Evaluate across code generation, document analysis, and structured output using pass@k, LLM-as-Judge, and RAG metrics. Supports Ollama, Google Gemini, Anthropic, and OpenAI.

python benchmarking machine-learning code-generation model-evaluation performance-benchmarking rag apple-silicon ollama llm-evaluation llm-testing hardware-profiling
  • Updated Mar 6, 2026
  • Python

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