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NVIDIA Nemotron 3.5 Lightning Becomes Available on Amazon SageMaker JumpStart

NVIDIA Nemotron 3.5 Lightning, an open model designed for high-volume agentic workloads, is now available for deployment in Amazon SageMaker JumpStart.

This article was AI-generated and published automatically. Context, labelling and all sources at the end of the article.

(KI-generiertes Symbolbild: Gemini / AI Connect)

Amazon Web Services has added NVIDIA Nemotron 3.5 Lightning to its model hub. The release introduces "an open model built for high-volume agentic workloads" directly into Amazon SageMaker JumpStart, allowing users to configure and deploy the system within their existing cloud environments.

The underlying architecture is configured as a "30B Mixture-of-Experts model (3B active)" designed to optimize resource usage during continuous inference. According to AWS, the model "delivers up to 4x higher throughput and up to 30% faster task completion for always-on agents" running automated tasks.

What this means for you

This integration gives cloud developers access to a specialized Mixture-of-Experts model directly within SageMaker JumpStart. Organizations deploying persistent autonomous agents may benefit from lower latency and increased throughput without leaving their existing AWS deployment workflows.

Evidence

Solidly sourced
46/100

  • NVIDIA Nemotron 3.5 Lightning is an open model for high-volume agentic workloads now accessible on SageMaker JumpStart.

    single source
    Quote

    „NVIDIA Nemotron 3.5 Lightning, an open model built for high-volume agentic workloads, is now available in Amazon SageMaker JumpStart.“

    AWS, Machine Learning

  • The model features a 30B Mixture-of-Experts architecture with 3B active parameters.

    single source
    Quote

    „30B Mixture-of-Experts model (3B active)“

    AWS, Machine Learning

  • The model provides up to 4x higher throughput and up to 30% faster task completion for always-on agents.

    single source
    Quote

    „delivers up to 4x higher throughput and up to 30% faster task completion for always-on agents“

    AWS, Machine Learning

Sources

The evidence score is computed, not hand-set: from confidence, the number of sources and the share of verified statements.

Source & transparency

As of: August 17, 2026

AI-generatedAI-generated: produced automatically from vetted sources with technical quality checks (source, quote and figure verification); no human sign-off of each item before publication

Sources
1
Verified statements
0 / 3
Evidence score
46Solidly sourced
Generative AIAI Agents

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