nemo-evaluator-sdk

To run reproducible LLM evaluation benchmarks across an HPC cluster, this framework executes assessments via Slurm using a container-first architecture. It ensures consistent and reliable results across local Docker, HPC, and cloud platforms.

Evaluating large language models supports 100+ benchmarks from 18+ harnesses. Coverage includes reasoning tasks like MMLU and GSM8K, coding evaluations like HumanEval, safety checks, and vision-language tasks for comprehensive model assessment.

Yes, you can use Docker containers to benchmark LLMs at scale locally. The framework supports multi-backend execution, allowing containerized enterprise-grade assessments on local Docker, Slurm HPC clusters, or cloud platforms.

Yes, you need the nemo-evaluator-launcher package and Docker installed to automate LLM benchmarking. These dependencies are required to set up the containerized architecture and execute the evaluation harnesses.

The best way to compare new LLMs on academic benchmarks before deployment is using a scalable benchmarking framework. It automates evaluation across 100+ benchmarks, generating standardized reports for efficient model comparison.