nemo-evaluator-sdk

To evaluate LLMs reproducibly across multiple benchmarks, you can run models against over 100 benchmarks using a container-first architecture that ensures consistent, repeatable performance results on local Docker or cloud platforms.

Yes, you can benchmark LLMs on a Slurm HPC cluster. The evaluation supports multi-backend execution, allowing you to run scalable benchmarking tasks directly on Slurm HPC clusters, local Docker, or cloud platforms.

Supported frameworks for LLM benchmarking include lm-evaluation-harness and vlmevalkit. These integrate with the platform to evaluate models across 18+ harnesses covering reasoning, coding, safety, and VLM tasks.

You can run LLM evaluations on local Docker by using the nemo-evaluator-launcher with a local execution configuration. This container-first approach sets up the environment to execute benchmarking tasks consistently.

Yes, LLM benchmarking supports coding and reasoning tasks like HumanEval and GSM8K. You can compare model performance specifically against these benchmarks to assess coding accuracy and mathematical reasoning capabilities.