NVIDIALlama Nemotron Embed 1B V2
NVIDIA's Llama Nemotron Embed 1B V 2 is an open-source embedding model optimized for multilingual and cross-lingual text question-answering retrieval, capable of handling long documents up to 8192 tokens with dynamic embedding size. It is genuinely best at transforming textual information into dense vector representations, making it a crucial component of a text retrieval system.
Input
Output
Context
8K
Max Output
n/a
Parameters
1.2B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Resources & Links
Estimated GPU requirements for Llama Nemotron Embed 1B V2
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 Nemotron Embed 1B V2
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama Nemotron Embed 1B V2 advertises a context window of 8,192 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Llama Nemotron Embed 1B V2 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 Nemotron Embed 1B V2 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 Nemotron Embed 1B V2 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 Nemotron Embed 1B V2 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 1.7 GB at INT8 for the weights and a default context, from the catalog's 1.2B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 14, 2026
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