USD Search NIM

NVIDIA NIM microservice that answers OpenUSD questions and automatically generates OpenUSD-Python code from text prompts for 3D workflow automation.

NVIDIA's generalist embedding model achieving record 69.32 score on MTEB benchmark. Fine-tuned from Llama architecture with improved techniques for training LLMs as embedding models.

NVIDIA RAPIDS cuVS is a GPU-accelerated library for vector search and clustering with CUDA-optimized HNSW, IVF, CAGRA, and PQ implementations. Supports batch queries for high QPS, suited for large-scale similarity search in GenAI apps. Achieves up to 12x faster indexing and lower latency vs CPU-only alternatives like FAISS CPU.

Universal text embedding model from NVIDIA achieving state-of-the-art performance on MMTEB leaderboard, optimized for retrieval, reranking, semantic similarity, and classification with 4,096-dimensional embeddings.

GPU-accelerated vector search library from NVIDIA providing approximate nearest neighbors and clustering algorithms with up to 12x faster index builds and 4.7x lower search latency through GPU parallelization.

AI-powered code editor and IDE built on VSCode with Composer 1.5 for multi-file editing, Background Agents for autonomous coding, and support for frontier models from OpenAI, Anthropic, Gemini, and xAI.