Video Codec SDK
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Mastering Agentic Techniques: AI Agent Evaluation | NVIDIA Technical BlogWhat is NVIDIA Model Optimizer?
The NVIDIA Model Optimizer (ModelOpt) library incorporates state-of-the-art model optimization techniques to compress and accelerate AI models. These techniques include quantization, distillation, pruning, speculative decoding, and sparsity. ModelOpt accepts Hugging Face, PyTorch, or ONNX format models as input and provides Python APIs for users to easily combine different optimization techniques to produce optimized checkpoints. ModelOpt supports highly performant quantization formats such as FP4, FP8, INT8, and INT4, and advanced algorithms including SmoothQuant, AWQ, SVDQuant, and Double Q
Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer | NVIDIA Technical BlogWhat is CLIP?
CLIP (Contrastive Language-Image Pretraining), introduced by OpenAI in 2021, is a foundation vision language model (VLM) that learns a shared embedding space for images and text through contrastive learning on large image-text pairs. Its ability to produce semantically aligned representations has made it a core building block across modern multimodal systems. The CLIP text encoder is widely reused as a conditioning module for text-to-image (Stable Diffusion, for example) and text-to-video (AnimateDiff, for example) synthesis. Its vision encoder serves as the visual backbone in multimodal LLMs
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