NVIDIA has made its Nemotron 3.5 Lightning model available through Amazon SageMaker JumpStart, enabling users to deploy the model without configuring serving infrastructure. The model is designed for fast, specialized execution in high-volume agentic workloads. It is described as the fastest open model in its class, delivering up to 4x higher throughput and up to 30% faster task completion for such workloads. The model, with 30B total parameters and 3B active parameters, can run on a single supported GPU, making it suitable for repetitive, specialized steps in agent workflows. According to NVIDIA, the model is distilled from its frontier Nemotron 3 Ultra and developed with the Nemotron Coalition, using a hybrid Mixture-of-Experts (MoE) architecture. It is trained on open datasets and released as an open model, allowing users to customize it and deploy it wherever their agents run.
NVIDIA emphasizes that not every agent step requires a frontier model. Always-on agents continuously gather context, observe their environment, reason, and act. While some steps may need advanced reasoning, many tasks such as classifying alerts or extracting form fields can be handled by smaller, specialized models. Running all steps through a single large model can add unnecessary cost and latency. A system-of-models approach can route each step to a model suited for the task. Nemotron 3.5 Lightning is built for the high-volume end of this system, with its MoE architecture allowing 3B parameters to be active per forward pass, helping maintain high throughput across long sessions. DFlash speculative decoding further reduces per-token latency, and the 1M-token context window allows agents to carry state across long-running sessions without repeated re-grounding.
The model's accuracy across reasoning and agentic benchmarks is reported by NVIDIA, with results measured under a consistent harness. The model's performance is compared against BF16 and NVFP4 variants across several benchmarks, though the results may differ from vendors' self-reported numbers. Organizations can post-train the model with NVIDIA NeMo for domain-specific tools and workflows, then deploy the resulting model in their chosen environment. The model is tailored for specialized, high-frequency work within agent workflows, including personal assistants, financial services, cybersecurity operations, telecom, and retail applications. Users can deploy the model through Amazon SageMaker JumpStart without manual configuration, or via the Hugging Face model page.
Source: awsml
