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# NVIDIA Nemotron 3.5 Lightning model is now available on Amazon SageMaker JumpStart

**[AWS](https://daily.dev/sources/aws)** · 2 min read · 0 upvotes · 0 comments

## Summary

NVIDIA's Nemotron 3.5 Lightning model is now available on Amazon SageMaker JumpStart. Built on a hybrid Mixture-of-Experts (MoE) architecture with 30B total parameters and only 3B active per forward pass, it delivers up to 4x throughput (~410 tokens/sec) and 30% faster task completion than comparable models. It supports up to 1M token context via DFlash speculative decoding and is designed for persistent agent workloads including financial document processing, cybersecurity triage, and telecom operations. The model is fully open-trained, allowing enterprises to post-train and deploy across edge, on-premises, or cloud environments. AWS customers can deploy it via the SageMaker JumpStart console or Python SDK.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://aws.amazon.com/about-aws/whats-new/2026/01/nvidia-nemotron-3.5-lightning-on-sagemaker-jumpstart>

## Questions this post answers

### What are the key specs and performance numbers for NVIDIA Nemotron 3.5 Lightning?

Nemotron 3.5 Lightning uses a hybrid Mixture-of-Experts architecture with 30B total parameters and only 3B active per forward pass. It achieves approximately 410 tokens/sec throughput (up to 4x over comparable models) and 30% faster task completion. It supports up to 1M tokens of context via DFlash speculative decoding and was distilled from Nemotron 3 Ultra.

_Engineers evaluating LLMs for agent pipelines track model specs and benchmarks like these on daily.dev._

### How do I deploy NVIDIA Nemotron 3.5 Lightning on Amazon SageMaker JumpStart?

Nemotron 3.5 Lightning can be deployed on SageMaker JumpStart either through the SageMaker JumpStart model catalog in the SageMaker console with a few clicks, or programmatically using the SageMaker Python SDK. The model is available to AWS customers for enterprise AI workloads including agent automation, financial document processing, and cybersecurity triage.

_Teams shipping LLM deployments on AWS find relevant SageMaker updates on daily.dev._

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---

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