Select your cookie preferences

We use essential cookies and similar tools that are necessary to provide our site and services. We use performance cookies to collect anonymous statistics, so we can understand how customers use our site and make improvements. Essential cookies cannot be deactivated, but you can choose “Customize” or “Decline” to decline performance cookies.

If you agree, AWS and approved third parties will also use cookies to provide useful site features, remember your preferences, and display relevant content, including relevant advertising. To accept or decline all non-essential cookies, choose “Accept” or “Decline.” To make more detailed choices, choose “Customize.”
Sign in
Sign in
or
Create a new account
About
Categories
Delivery Methods
Solutions
Resources
 Your Saved List Become a Channel Partner Sell in AWS Marketplace Amazon Web Services Home Help

Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

product logo

Llama-3.2-NV-EmbedQA-1B-v2

By:
NVIDIA
Latest Version:
v1.3.1
Multilingual text question-answering retrieval, transforming textual information into dense vector representations.
Continue to Subscribe

    Product Overview

    The NVIDIA NeMo Retriever Llama3.2 embedding model is optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 8192 tokens) and dynamic embedding size (Matryoshka Embeddings). This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish. In addition to enabling multilingual and cross-lingual question-answering retrieval, this model reduces the data storage footprint by 35x through dynamic embedding sizing and support for longer token length, making it feasible to handle large-scale datasets efficiently. For additional information please contact NVIDIA: https://www.nvidia.com/en-us/data-center/lp/aws-marketplace-offer This model is ready for commercial use.

    Key Data

    Version
    v1.3.1
    Usage Instructions
    Release Notes
    By
    NVIDIA
    Categories
    Generative AI
    Embedding/Feature Extraction
    Embeddings
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • The NeMo Retriever Llama3.2 embedding model is most suitable for users who want to build a multilingual question-and-answer application over a large text corpus, leveraging the latest dense retrieval technologies.

    • NVIDIA NIM, a part of the NVIDIA AI Enterprise software platform available on the AWS Marketplace, is a set of easy-to-use microservices designed for secure, reliable deployment of high performance AI model inferencing.

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$4.00/hr

    running on ml.g5.12xlarge

    Model Batch Transform$4.00/hr

    running on ml.g5.12xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$7.09/host/hr

    running on ml.g5.12xlarge

    SageMaker Batch Transform$7.09/host/hr

    running on ml.g5.12xlarge

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.g5.12xlarge
    Vendor Recommended
    $4.00
    ml.g5.24xlarge
    $4.00

    Usage Information

    Model input and output details

    Input

    Summary

    The model accepts JSON requests that specifies the input text to be embedded.

    { "input": ["Hello world"], "model": "nvidia/llama-3.2-nv-embedqa-1b-v2", "input_type": "query" }'

    Input MIME type
    application/json
    Sample input data
    Expand all input descriptions
    Field name
    input
    Description
    Input text to embed. Max length is 512 tokens.
    Required
    No
    Data type
    FreeText
    Default value
    ""
    Field name
    model
    Description
    ID of the embedding model.
    Required
    Yes
    Data type
    FreeText
    Field name
    input_type
    Description
    passage is used when generating embeddings during indexing. query is used when generating embeddings during querying.
    Required
    No
    Data type
    Categorical
    Default value
    passage
    Allowed values
    passage, query
    Field name
    encoding_format
    Description
    The format to return the embeddings in.
    Required
    No
    Data type
    Categorical
    Default value
    float
    Allowed values
    float, base64
    Field name
    truncate
    Description
    Specifies how inputs longer than the maximum token length of the model are handled.
    Required
    No
    Data type
    Categorical
    Default value
    none
    Allowed values
    NONE, START, END

    Output

    Summary

    The output is a JSON object that has an array of embeddings for the input texts.

    Output MIME type
    application/json
    Sample output data
    Expand all output descriptions
    Field name
    embeddings
    Description
    An array of embeddings, where each embedding is an array of floats. The length of the embeddings array will be the same as the length of the original texts array.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range

    Sample notebook

    Additional Resources

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    Llama-3.2-NV-EmbedQA-1B-v2

    Free support via NVIDIA NIM Developer Forum: https://forums.developer.nvidia.com/c/ai-data-science/nvidia-nim/ Global enterprise support with NVIDIA AI Enterprise subscription: https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/

    AWS Infrastructure

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

    Learn More

    Refund Policy

    No refund

    Customer Reviews

    There are currently no reviews for this product.
    View all