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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 stars5,49746,180Highest
7-day net stars——
Fork7926,162Highest
30-day trendInsufficient historyInsufficient history
Maintenance
Last push2026-11-032024-08-16
Maintenance notesNot verifiedNot verified
Repository created2024-04-232020-05-20
License & use
LicenseApache-2.0 · PermissiveMPL-2.0 · Share derivative source
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.Check current official software, hosting, model and infrastructure pricing.
Pricing source——
First collected2026-11-032026-11-03
MetricRepository stars, not unique users or individual-skill installsRepository stars, not unique users or individual-skill installs
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.🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Measured at2026-11-03 08:29 UTC2026-11-03 08:23 UTC
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