---
格式版本: 2
标题: "NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents"
原文链接: "https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T20:58:24+08:00"
入库时间: "2026-08-12T12:58:42.151Z"
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---

[Long-running AI agents](https://www.nvidia.com/en-us/glossary/ai-agents/) spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning model for every execution step adds cost and latency.

[NVIDIA Nemotron 3.5 Lightning](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4) is an open 30B [mixture-of-experts (MoE)](https://www.nvidia.com/en-us/glossary/mixture-of-experts/) model with 3B active parameters built for that execution layer of always-on agents. It is designed for harnesses like OpenClaw and Hermes Agent—all supported by the [NVIDIA NemoClaw](https://github.com/NVIDIA/NemoClaw) open source security and management stack for running always-on AI agents.

The [NVIDIA Nemotron](https://developer.nvidia.com/topics/ai/nemotron) open model family is like a software library, with each release continuously improving accuracy and speed. As these models evolve, the rapid maturation of model routing and orchestration is also underway.

This is important because developers increasingly build applications with a [system of models](https://www.nvidia.com/en-us/glossary/multi-agent-systems/#:~:text=request%20is%20inputted.-,System%20of%20Models,-Depending%20on%20the). [Frontier reasoning models](https://www.nvidia.com/en-us/glossary/frontier-models/) such as [Nemotron 3 Ultra](https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-powers-faster-more-efficient-reasoning-for-long-running-agents/) handle orchestration and complex planning while smaller, more efficient models handle the high-volume execution layer.

This post introduces NVIDIA Nemotron 3.5 Lightning and explains how its smaller MoE design is optimized for high-volume, low-latency execution in autonomous agents. It also details the inference and training innovations that power it. Finally, the post also introduces [NVIDIA NeMo Switchyard](https://github.com/NVIDIA-NeMo/Switchyard), a library that intelligently routes each task to the best model for the job.

## Why is Nemotron 3.5 Lightning ideal for long-running AI agents?

Nemotron 3.5 Lightning is a customizable open 30B MoE model with 3B active parameters, providing optimal high-volume execution for autonomous agents. MoEs are fast and efficient because a router sends each token to just a few of its many experts, so only a fraction of the model’s parameters run per token. This provides the capacity of a larger dense model at the compute cost of a small one.

Nemotron 3.5 Lightning is the smallest member of the Nemotron 3 model family and ships with many of the same techniques proven across the family, including:

- [**Speculative decoding**](https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/)**:** Multi-token prediction was included during Nemotron 3.5 Lightning training (as for Nemotron 3 Super and Nemotron 3 Ultra). Nemotron 3.5 Lightning also ships with DFlash and DSpark, enabling more comprehensive [inference optimization](https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/) across a range of serving scenarios.
- **Harness-optimized training:** The model is trained for popular agent harnesses, enabling agents to make more accurate calls while reducing latency for high-volume tasks.

The result is a model built for execution-focused, high call volumes, and low latency—all at a size that deploys anywhere from an [NVIDIA DGX Spark](https://www.nvidia.com/en-us/products/workstations/dgx-spark/) to a data center.

![](https://www.youtube.com/watch?v=Lhy1Bu8fN8A)

**Video 1. Learn how to deploy NVIDIA Nemotron 3.5 Lightning on DGX Spark and use it for fast, high-volume agentic workloads**

## Customize Nemotron 3.5 Lightning out of the box

Models earn their place in [specialized AI](https://www.nvidia.com/en-us/glossary/specialized-ai/) agent systems when they’re adapted to the job. And Lightning-class models are highly customizable: small models fine-tune faster, cheaper, and on far more modest hardware than their larger counterparts.

You can customize Nemotron 3.5 Lightning out of the box to fit your workload. As with every Nemotron [open model](https://www.nvidia.com/en-us/glossary/open-models/) launch, the weights, training data, and recipes are released as permissively as possible under OpenMDW-1.1, so you can:

- Fine-tune with LoRA or full SFT using [NeMo Automodel](https://github.com/nvidia-nemo/automodel) and [NeMo Megatron Bridge](https://github.com/NVIDIA-NeMo/Nemotron/tree/main/usage-cookbook/Nemotron-3.5-Lightning/lora-text2sql)
- Run reinforcement learning and environment-based evaluations and rollouts with [NeMo RL](https://github.com/nvidia-nemo/rl) and [NeMo Gym](https://github.com/NVIDIA-NeMo/gym)

This release includes [Nemotron-RL Agentic Terminal Pivot](https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Terminal-Pivot-v1-nano35-release), an open agentic reinforcement learning dataset used to train some of the coding agent capabilities.

## Route work to the right model using NeMo Switchyard

While frontier models may win the headlines, models like Nemotron 3.5 Lightning earn their medals in the trenches. They handle requests like git pull, validate tool outputs, format results, and run the routine calls that dominate any long-running agent’s token budget.

Model routing and orchestration help make this division of labor more accessible. They are now available through [NVIDIA NeMo Switchyard](https://developer.nvidia.com/blog/route-ai-agent-workloads-across-models-with-nvidia-nemo-switchyard). Switchyard can expose Nemotron 3.5 Lightning as a routing target alongside your open and closed models, so every request lands on the most capable and efficient model that can handle it. Plans route up to the frontier, execution routes down to Lightning, ensuring that your tokens are spent efficiently and effectively.

## How does Nemotron 3.5 Lightning perform on the accuracy-speed Pareto frontier?

Nemotron 3.5 Lightning delivers leading accuracy at the highest output speed in its class, winning the accuracy-versus-speed Pareto frontier on the Artificial Analysis Intelligence Index. This index combines nine evaluations to measure model performance across agentic tasks, coding, scientific reasoning, and general intelligence.

Nemotron 3.5 Lightning combines strong intelligence with up to 4x output speed of similar-sized models, placing it on the accuracy-speed Pareto frontier for high-volume agent workloads.

![Artificial Analysis Intelligence Index versus output speed scatter, with Nemotron 3.5 Lightning in the winning quadrant.
](https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/artificial-analylsis-intelligence-index-versus-output-speed-nemotron-3.5-lightning.webp)

Figure 1. Nemotron 3.5 Lightning defines the accuracy-speed Pareto frontier for small open models on the Artificial Analysis Intelligence Index leaderboard

Agent efficiency ultimately comes down to how quickly a model completes useful work and not simply how fast it generates tokens. On PinchBench, Nemotron 3.5 Lightning reaches 86% accuracy while completing 10,000 tasks 30% faster than Qwen3.6 35B at similar accuracy.

Higher [inference](https://www.nvidia.com/en-us/glossary/ai-inference/) throughput and [token](https://blogs.nvidia.com/blog/ai-tokens-explained/) efficiency places Nemotron 3.5 Lightning on the efficiency frontier, helping always-on agents finish high-volume work faster.

![Chart comparing PinchBench accuracy with time to complete 10,000 tasks. Nemotron 3.5 Lightning reaches similar accuracy as Qwen3.6 35B 30% faster. 
](https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/nemotron-3.5-lightning-efficiency-frontier-agentic-tasks.webp)

Figure 2. Nemotron 3.5 Lightning leads the efficiency frontier by completing agentic tasks up to 30% faster at comparable accuracies

## How does Nemotron 3.5 Lightning deliver speed without compromising accuracy?

Nemotron 3.5 Lightning delivers speed and customization without compromising accuracy through speculative decoding, and quantization.

### Speculative decoding

Nemotron 3.5 Lightning is built to quickly generate tokens and has the ability to generate multiple tokens through speculative decoding. This is a process whereby the model, or draft model, will draft some number of tokens which are efficiently reviewed. Nemotron 3.5 Lightning underwent a dedicated pretraining stage to bake multi-token prediction (MTP) into the model, as with Nemotron 3 Super and Ultra. After training, a dedicated MTP-boosting phase further improved MTP accuracy.

Beyond MTP, two draft models are provided with Nemotron 3.5 Lightning: [DSpark](https://arxiv.org/pdf/2607.05147), which is recommended for DGX Spark inference workloads and low concurrency data center workloads. MTP is best suited for medium to high concurrency, with the optimal draft length decreasing as concurrency increases. NVIDIA is also releasing a [DFlash](https://developer.nvidia.com/blog/boost-inference-performance-up-to-15x-on-nvidia-blackwell-using-dflash-speculative-decoding/) draft model, which can be measured against the others and may perform best for your workloads.

### Quantization

Nemotron 3.5 Lightning ships with an [NVFP4](https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/) checkpoint alongside BF16, using the same specialized NVFP4 kernels that power Nemotron 3 Ultra across NVIDIA Blackwell, NVIDIA Hopper, and NVIDIA Ampere GPUs. The same file serves just as well in data centers as it does on your desktop DGX Spark.

## How is Nemotron 3.5 Lightning ideal for local AI?

Nemotron 3.5 Lightning makes capable agentic AI accessible on local systems including NVIDIA Jetson, GeForce RTX 5090, and DGX Spark.

NVIDIA has worked with a number of teams including EXO Labs to understand how this model performs on DGX Spark.

![A scatter/line chart showing the DGX Spark performance “Intelligence / Speed Frontier,” plotting utilization over task time. It compares several model configurations along a Pareto frontier curve, with callouts indicating parameter counts and results.
](https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/local-ai-dgx-spark-frontier-with-nemotron-3.5-lightning-1.webp)

Figure 3. Nemotron 3.5 Lightning sits right on the Pareto frontier for small open models on the EXO Labs local.ai leaderboard

In addition, you can run Nemotron 3.5 Lightning across the industry standard set of tools, including LM Studio, llama.cpp, Ollama, and Unsloth.

## Partner ecosystem

Nemotron 3.5 Lightning is supported by a growing ecosystem of partners across harnesses, customization, deployment, and inference, including:

- **Post-training:** [AgileRL](https://www.agilerl.com/blog/partnering-with-nvidia-for-post-training-nemotron-models-to-beat-frontier-apis), Applied Compute, Deep Cogito, [distil labs](https://www.distillabs.ai/blog/the-best-mid-size-model-to-fine-tune-was-the-third-best-model-to-prompt/), [Fastino Labs](http://fastino.ai/blog/fastino-nemotron-3-5-lightning-finance-and-healthcare), [Locai Labs](https://locailabs.com/blog/introducing-locai-one-ai-in-a-box/), Prime Intellect, [Reasonable](https://reasonable.io/blog/verification-at-lightning-speed-nemotron), Thinking Machines Lab, [Thoughtworks](https://www.thoughtworks.com/insights/blog/generative-ai/putting-nvidia-nemotron-3-5-lightning-test), [Trajectory](http://trajectory.ai/field-notes/continual-learning-cheap-enough-to-meter), [Uniphore](http://uniphore.com/blog/testing-nvidia-nemotron-3-5-lightning-against-real-enterprise-agentic-workloads/)
- **Inference software:** [Ollama](https://ollama.com/blog/nemotron-3-5-lightning), Exo, [Canonical](https://ubuntu.com/blog/nvidia-nemotron-3-5-lightning), [LM Studio](https://lmstudio.ai/models/nvidia/nemotron-3.5-lightning), Unsloth
- **Harnesses and agent frameworks:** [Aible](https://www.aible.com/Nemotron35-Lightning), [Cline](https://cline.bot/blog/nvidia-nemotron-3-5-lightning-available-in-cline)**,** [Factory AI](https://factory.ai/news/nvidia-dgx-spark), Hermes Agent, [Kilo Code](https://blog.kilo.ai/nvidia-nemotron-3-5-lightning), [LangChain](https://www.langchain.com/blog/switchyard-agent-routing-benchmark), LM Studio Bionic, OpenClaw, [OpenCode](http://opencode.ai/), [OpenHands](https://www.openhands.dev/blog/nvidia-nemotron-3.5-lightning), [Pi](https://pi.dev/models/openrouter/nvidia-nemotron-3-5-lightning-free)
- **Cloud service provider platforms**: [Amazon SageMaker JumpStart](https://aws.amazon.com/about-aws/whats-new/2026/01/nvidia-nemotron-3.5-lightning-on-sagemaker-jumpstart/), [Google Cloud Gemini Enterprise Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform), MSFT Foundry, OCI Enterprise AI
- **GSI:** Accenture, [Tata Consultancy Services](https://www.tcs.com/insights/blogs/tcs-accelerates-drug-discovery-with-molecular-intelligence-model), Tech Mahindra, Wipro
- **AI natives:** [Arcos Labs](https://www.arcoslabs.com/blog/arcos-law-on-premise-ai-nvidia-nemotron/), [CodeRabbit](https://www.coderabbit.ai/blog/teaching-nvidia-nemotron-3-5-lightning-to-route-code-reviews), Dream, Harvey
- **Hosted inference service providers:** [Baseten](https://www.baseten.co/blog/introducing-nemotron-35-lightning/), BlackBox AI, [CoreWeave](https://wandb.ai/inference/coreweave/cw_nvidia_Nemotron-3.5-Lightning-30B-A3B), [Crusoe](https://www.crusoe.ai/cloud/managed-inference#:~:text=NVIDIA,5%20MODELS), [DeepInfra](https://deepinfra.com/blog/nvidia-nemotron-3.5-lightning-release), [Fireworks AI](http://fireworks.ai/models/fireworks/nemotron-lightning-3p5-30b-a3b), [FriendliAI](https://friendli.ai/blog/nvidia-nemotron-3.5-Lightning), [GMI Cloud](https://www.gmicloud.ai/en/blog/nvidia-nemotron-3-5-lightning-is-live-on-gmi-cloud-what-your-agent-system-was-missing), [Modal](https://modal.com/endpoints), [Nebius](https://tokenfactory.nebius.com/endpoints?modals=endpoint-details&model-id=nvidia/Nemotron-3_5-Lightning), [Together AI](https://www.together.ai/models/nemotron-3-5-lightning)

## Start building with Nemotron 3.5 Lightning

Nemotron 3.5 Lightning is fully open—weights, data, and recipes—so you can adapt it to your workflows and deploy it anywhere. To get started, try it on [build.nvidia.com](https://build.nvidia.com/nvidia/nemotron-3.5-lightning-30b-a3b) or through [OpenRouter](https://openrouter.ai/nvidia/nemotron-3.5-lightning:free). Download the weights from [Hugging Face](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4), and [ModelScope](https://modelscope.ai/collections/nv-community/Nemotron-35-Lightning). Want to dive deeper?

- Read the [Nemotron 3.5 Lightning cookbook](https://github.com/NVIDIA-NeMo/Nemotron/tree/main/usage-cookbook/Nemotron-3.5-Lightning)
- Deploy with the [vLLM](https://github.com/NVIDIA-NeMo/Nemotron/blob/main/usage-cookbook/Nemotron-3.5-Lightning/vllm_cookbook.ipynb), [SGLang](https://github.com/NVIDIA-NeMo/Nemotron/blob/main/usage-cookbook/Nemotron-3.5-Lightning/sglang_cookbook.ipynb), and [TensorRT-LLM](https://github.com/NVIDIA-NeMo/Nemotron/blob/main/usage-cookbook/Nemotron-3.5-Lightning/trtllm_cookbook.ipynb) deployment guides
- Set up routing with [Switchyard docs](https://github.com/NVIDIA-NeMo/Switchyard/blob/main/docs/getting_started.md)

*Stay up to date on* [*NVIDIA Nemotron*](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) *by subscribing to* [*NVIDIA news*](https://www.nvidia.com/en-us/ai-data-science/generative-ai/news/) *and following NVIDIA AI on* [*LinkedIn*](https://www.linkedin.com/showcase/nvidia-ai/posts/?feedView=all)*,*[*X*](https://x.com/NVIDIAAIDev)*,*[*Discord*](https://discord.com/invite/nvidiadeveloper)*, and* [*YouTube*](https://www.youtube.com/@NVIDIADeveloper)*.*

*Visit the* [*Nemotron developer page*](https://developer.nvidia.com/nemotron) *for resources to get started. Explore open Nemotron models and datasets on* [*Hugging Face*](https://huggingface.co/collections/nvidia/nvidia-nemotron-v3)*,* [*ModelScope*](https://modelscope.ai/collections/nv-community/Nemotron-35-Lightning)*, and* [*Blueprints*](https://build.nvidia.com/blueprints) *on* [*build.nvidia.com*](https://build.nvidia.com/nvidia/nemotron-3.5-lightning-30b-a3b)*.*

*Engage with* [*Nemotron livestreams*](https://www.youtube.com/playlist?list=PL5B692fm6--vEL0FwctKghCpyEnBGAQJA)*,*[*tutorials*](https://www.youtube.com/playlist?list=PL5B692fm6--vdRKB14FImVi7MTJ77zjn4)*, and the developer community on the* [*NVIDIA forum*](https://forums.developer.nvidia.com/c/ai-data-science/nvidia-nemotron/669) *and* [*Discord*](https://discord.com/invite/nvidiadeveloper)*.*
