NVIDIA/RULER

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1609 stars · 140 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RULER is a benchmark framework from NVIDIA that generates synthetic test examples to measure the true effective context size of long-context language models. It provides configurable sequence lengths and 13 tasks across 4 categories, with reference results for many open-source and proprietary models.

Use cases

• measure the real effective context length of an LLM
• benchmark long-context language models beyond simple needle-in-a-haystack tests
• evaluate how model performance degrades at 32k, 64k, and 128k tokens
• compare claimed vs actual context window sizes across models
• generate synthetic long-context evaluation datasets
• regression-test a fine-tuned model's long-context capabilities

When to choose

• you need rigorous, configurable evaluation of long-context abilities
• you are comparing models' effective context sizes before deployment
• you want reproducible synthetic tasks instead of ad-hoc prompts

When to avoid

• you need general-purpose LLM benchmarks unrelated to context length
• you lack GPU resources to run local model inference
• you only need simple needle-in-a-haystack checks

Facets

library · maturity active

benchmarking llm-inference data-generation testing large-language-models machine-learning artificial-intelligence developer-tools python long-context evaluation synthetic-data llm-benchmark context-window linux gpu

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