Llama Nemotron Rerank Vl 1B V2
NVIDIA's Llama Nemotron Rerank Vl 1B V 2 is a multimodal rerank model optimized for question-answering retrieval, capable of processing text, images, or combined inputs. It is particularly suited for multimodal question-and-answer applications over large corpora, leveraging dense retrieval technologies.
Input
Output
Context
10K
Max Output
-
Parameters
1.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Llama Nemotron Rerank Vl 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 Rerank Vl 1B V2
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Llama Nemotron Rerank Vl 1B V2 advertises a context window of 10,240 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 Nemotron Rerank Vl 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 Rerank Vl 1B V2 accepts Text and produces Text. 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 Rerank Vl 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.
Yes. Llama Nemotron Rerank Vl 1B V2 is served on the managed pool through the OpenAI-compatible endpoint as nvidia/llama-nemotron-rerank-vl-1b-v2, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 2.3 GB at INT8 for the weights and a default context, from the catalog's 1.7B 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: Sep 13, 2026
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