NVIDIA
NVIDIA
llama-3.3-70b-instruct-pb6
Container
NVIDIA
NVIDIA
llama-3.3-70b-instruct-pb6

This container houses the Llama-3.3-70B-Instruct, which is an auto-regressive language model that uses an optimized transformer architecture.

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Llama-3.3-70B-Instruct PB6

Description:

This container houses the Llama-3.3-70B-Instruct, which is an auto-regressive language model that uses an optimized transformer architecture. It is designed for text-based tasks such as multilingual chat, coding assistance, and synthetic data generation, and is particularly optimized for dialogue-based use cases. With 70 billion parameters, it provides strong performance that is comparable to larger models but with lower hardware requirements, and it does not process images or audio.

The Llama-3.3-70B-Instruct NIM Production Branch, exclusively available with NVIDIA AI Enterprise, is a 9-month supported, API-stable branch that includes monthly fixes for high and critical software vulnerabilities. This branch provides a stable and secure environment for building your mission-critical AI applications. The Llama-3.3-70B-Instruct NIM production branch releases every six months with a three-month overlap in between two releases.

The container components are ready for commercial/non-commercial use.

Documentation

Visit the NIM Container LLM page for release documentation, deployment guides, and more.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see (meta-llama/Llama-3.3-70B-Instruct · Hugging Face).

License/Terms of Use:

GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products.

The use of any models in this container is governed by the NVIDIA Open Model Agreement in addition to any terms which govern the specific models used.

You are responsible for ensuring that your use of any provided models complies with all applicable laws.

Use of this model is governed by the NVIDIA Open Model Agreement. The underlying model is also licensed under the Llama 3.3 Community License Agreement. Built with Llama.

Deployment Geography:

Global

Release Date:

Build.NVIDIA.com 12/17/2024 via
llama-3.3-70b-instruct Model by Meta | NVIDIA NIM

Github 12/13/2024 via
https://github.blog/changelog/2024-12-13-llama-3-3-70b-instruct-is-now-available-on-github-models-ga/

Huggingface 12/06/2024 via
https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct

Llama-3.3-70B-Instruct

Llama-3.3-70B-Instruct Container includes the following model:

Model Name & LinkUse CaseHow to Pull the Model
Llama-3.3-70B-InstructA powerful conversational AI model for tasks like chat, question answering, and coding assistance.Automatic

Deployment Details:

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.
For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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Publisher
NVIDIA
NVIDIA
LicenseNVIDIA proprietary
Latest Tag2.0.4-pb6.0
UpdatedMay 28, 2026 UTC
Compressed Size12.04 GB
Multinode SupportNo
Multi-Arch SupportYes

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