Llama Embed Nemotron 8B
Llama Embed Nemotron 8B is a text embedding model built by NVIDIA, optimized for retrieval, reranking, semantic similarity, and classification tasks. It is designed as a foundational component for Retrieval-Augmented Generation (RAG) systems and has robust multilingual and cross-lingual capabilities.
Input
Output
Context
33K
Max Output
-
Parameters
7.5B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Llama Embed Nemotron 8B
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Llama Embed Nemotron 8B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama Embed Nemotron 8B advertises a context window of 32,768 tokens. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Llama Embed Nemotron 8B is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Llama Embed Nemotron 8B accepts Text and produces Embedding. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Llama Embed Nemotron 8B is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Llama Embed Nemotron 8B is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 7.7 GB at INT8 for the weights and a default context, from the catalog's 7.5B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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