# Nvidia

> Browse 6 items tagged with Nvidia.

## Items

- [NV-Embed](https://vectordb.ever.works/de/items/nv-embed) — 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.
- [cuVS](https://vectordb.ever.works/de/items/cuvs) — 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.
- [USD Code NIM](https://vectordb.ever.works/de/items/usd-code-nim) — NVIDIA NIM microservice that answers OpenUSD questions and automatically generates OpenUSD-Python code from text prompts for 3D workflow automation.
- [USD Search NIM](https://vectordb.ever.works/de/items/usd-search-nim) — NVIDIA NIM microservice enabling natural language and image-based search through massive libraries of OpenUSD, 3D, and image data for content discovery.
- [Llama-Embed-Nemotron-8B](https://vectordb.ever.works/de/items/llama-embed-nemotron-8b) — 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.
- [RAPIDS cuVS](https://vectordb.ever.works/de/items/rapids-cuvs) — 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.

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_Canonical page: https://vectordb.ever.works/de/tags/nvidia_