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Nemotron-4-340B

by Community

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Nemotron-4-340B

Added 1 June 2026

Overview

Nemotron-4-340B is an open-source large language model with 340 billion parameters, fine-tuned for instruction following. Released to the community via Hugging Face, it serves as a foundation for building conversational AI and reasoning applications.

Best for

Best for
Developers and researchers who need a powerful, open foundation model for instruction following and reasoning

Use cases

  • Building custom instruction-following chatbots
  • Generating synthetic data for fine-tuning smaller models
  • Performing complex reasoning tasks in research or prototypes

Notes

Nemotron-4-340B is an open-source large language model with 340 billion parameters, fine-tuned for instruction following. Released to the community via Hugging Face, it serves as a foundation for building conversational AI and reasoning applications.

Use cases

  • Building custom instruction-following chatbots
  • Generating synthetic data for fine-tuning smaller models
  • Performing complex reasoning tasks in research or prototypes

Pros

  • Large 340B parameter scale delivers strong performance on reasoning and instruction tasks
  • Fully open source and freely available on Hugging Face for experimentation
  • Supports a wide range of NLP tasks out of the box

Cons

  • Requires substantial GPU resources for inference, not practical for edge devices
  • Community support may be less responsive than commercial vendor support
  • Large model size leads to higher latency and cost in production

Indexed from awesome-llm and enriched against its public facts.

Pros

  • Large 340B parameter scale delivers strong performance on reasoning and instruction tasks
  • Fully open source and freely available on Hugging Face for experimentation
  • Supports a wide range of NLP tasks out of the box

Cons

  • Requires substantial GPU resources for inference, not practical for edge devices
  • Community support may be less responsive than commercial vendor support
  • Large model size leads to higher latency and cost in production