Inferencia, implementación y tiempo de ejecución

Model-Optimizer

NVIDIA/Model-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.

★ 3,3KEstrellas
⑂ 513Bifurcaciones
318Problemas abiertos
PythonIdioma
Apache-2.0Licencia
Q@project.QualityScorePuntuación editorial

Resumen

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.

Requisitos, instalación y guía rápida

Los requisitos de instalación no se indican en los metadatos del repositorio.

Uso

El uso no se indica en los metadatos del repositorio.

Compatibilidad de modelos y casos de uso

La compatibilidad del modelo no se indica en los metadatos del repositorio.

Notas sobre la licencia y los riesgos

Apache-2.0

ToolAI metadata-only listing: repository metadata is public; editorial content and manual verification are still pending. Quality score was recomputed from live GitHub repository facts on 2026-08-22 UTC using the seven-dimension channel formula.

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