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NModel-Optimizer
A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
Popularity
Repository stars5,49767,972Highest
7-day net stars——
Fork79227,626Highest
30-day trendInsufficient historyInsufficient history
Maintenance
Last push2026-11-032026-11-02
Maintenance notesNot verifiedUnknown
Repository created2024-04-232010-08-17
License & use
LicenseApache-2.0 · PermissiveBSD-3-Clause · Permissive
PricingPricing not verifiedPricing not verified
Basics
Browse by purposeData analysisData analysis
TypeFrameworks & SDKsFrameworks & SDKs
SubcategoryData science & machine learningData science & machine learning
PlatformNot verifiedNot verified
Cost detailsCheck current official software, hosting, model and infrastructure pricing.Verify software, hosting, model and infrastructure costs against the current official documentation.
Pricing source——
First collected2026-11-032026-11-02
MetricRepository stars, not unique users or individual-skill installsRepository Stars, not unique users
LanguagePythonPython
About this projectA unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.Machine-learning algorithms and modeling tools for Python.
Measured at2026-11-03 08:29 UTC2026-11-02 19:20 UTC
License tiers are derived from metadata. Check obligations for your use; unknown does not mean prohibited. Ranked by repository stars, one position per repository. Stars and forks are not user counts.