Model overview
`llama-embed-nemotron-8b` is a versatile text embedding model trained by NVIDIA and optimized for retrieval, reranking, semantic similarity, and classification use cases. This model has robust capabilities for multilingual and cross-lingual text retrieval. It is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model achieves state-of-the-art performance on the multilingual MTEB leaderboard as of October 21, 2025. Together with the model weights, we're releasing the full recipe behind the `llama-embed-nemotron-8b`: - A detailed technical report focusing on our Synthetic Data Generation (SDG) pipeline and core design choices. - The training dataset, featuring a curated mix of public and synthetic data. - The full training code via the NeMo AutoModel framework.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
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Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。