sparsity | BestHub

DataFunSummit

Mar 14, 2024 · Artificial Intelligence

Multi‑Level Efficiency Challenges and Emerging Paradigms for Large AI Models

The article examines how large AI models are moving toward a unified, low‑knowledge‑density paradigm that raises computational efficiency challenges across model, algorithm, framework, and infrastructure layers, while also highlighting NVIDIA's GTC 2024 China AI Day sessions that showcase practical solutions and upcoming training opportunities.

AI InfrastructureAI conferencesModel Efficiency

0 likes · 10 min read

Multi‑Level Efficiency Challenges and Emerging Paradigms for Large AI Models

Bilibili Tech

Jun 13, 2023 · Artificial Intelligence

InferX Inference Framework and Its Integration with Triton for High‑Performance AI Model Serving

Bilibili’s self‑developed InferX framework, combined with NVIDIA Triton Inference Server, streamlines AI model serving by adding quantization, structured sparsity, and custom kernels, delivering up to eight‑fold throughput gains, cutting GPU usage by half, and enabling faster, cost‑effective OCR and large‑model deployments.

AI inferenceGPU utilizationInferX

0 likes · 10 min read

InferX Inference Framework and Its Integration with Triton for High‑Performance AI Model Serving

DataFunSummit

Sep 4, 2022 · Artificial Intelligence

Sparse Features in Machine Learning: Challenges, NVIDIA Ampere Structured Sparsity, Knowledge Distillation, and GAN Model Compression

This talk explores the challenges and opportunities of leveraging sparsity in machine learning models, covering fine‑grained and coarse‑grained sparsity, NVIDIA Ampere’s 2:4 structured sparsity, knowledge‑distillation techniques for converting unstructured to structured sparsity, and model compression strategies for generative adversarial networks.

GANGPU Accelerationdeep learning

0 likes · 14 min read

Sparse Features in Machine Learning: Challenges, NVIDIA Ampere Structured Sparsity, Knowledge Distillation, and GAN Model Compression

DataFunTalk

Mar 16, 2022 · Artificial Intelligence

Parameter-Efficient Sparsity Training for the PLUG Large-Scale Language Model

This article presents the PLUG 270‑billion‑parameter Chinese language model and introduces a parameter‑efficient sparsity training (PST) framework that combines unstructured and structured pruning with low‑rank decomposition to dramatically reduce model size while preserving downstream performance.

PLUGdeep learninglarge language models

0 likes · 13 min read

Parameter-Efficient Sparsity Training for the PLUG Large-Scale Language Model