{"slug": "nvidia-nemotron-3-5-lightning-now-available-on-amazon-sagemaker-jumpstart", "title": "NVIDIA Nemotron 3.5 Lightning Now Available on Amazon SageMaker JumpStart", "summary": "NVIDIA's Nemotron 3.5 Lightning, a 30-billion-parameter hybrid Mixture-of-Experts model that activates only 3 billion parameters per forward pass, is now available on Amazon SageMaker JumpStart, delivering up to 4x throughput at roughly 410 tokens per second and 30% faster task completion for persistent agent workloads. The model supports up to 1 million tokens of context via DFlash speculative decoding and is fully open-trained, allowing enterprises to post-train and deploy with complete ownership across edge, on-premises, or cloud infrastructure.", "body_md": "**August 11, 2026**, (Inside AI) — NVIDIA's **Nemotron 3.5 Lightning** is now available on **Amazon SageMaker JumpStart**, granting **AWS** customers streamlined access to what the company calls the fastest open model in its class for persistent agent workloads.\n\nThe model targets high-throughput enterprise automation, including personal assistants, financial document processing, cybersecurity triage, and telecom operations. Its hybrid **Mixture-of-Experts (MoE)** architecture packs **30 billion** total parameters but activates only **3 billion** per forward pass, delivering up to **4x** throughput at roughly **410 tokens per second** and **30%** faster task completion than comparable models.\n\nThis launch underscores a growing industry push to shrink agent latency without sacrificing capability. By integrating directly with popular agent harnesses and supporting up to **1 million** tokens of context via **DFlash** speculative decoding, Nemotron 3.5 Lightning aims to close the gap between research benchmarks and real-world deployment speed.\n\nDistilled from **Nemotron 3 Ultra**, the model is fully open-trained on open datasets. Enterprises can post-train it for proprietary tools, workflows, and policies, then deploy with complete ownership across edge, on-premises, or cloud infrastructure. That licensing flexibility contrasts with some competitors that impose usage restrictions even on open-weight releases.\n\n## MoE Efficiency Meets Persistent Agent Demands\n\nPersistent agents, systems that maintain context and state over long-running tasks, require both low latency and high throughput. Nemotron 3.5 Lightning’s sparse activation pattern means only a fraction of parameters fire for any token, slashing compute costs while preserving quality on reasoning-heavy chains. Early adopters in cybersecurity triage, for example, can parse massive log streams and correlate threats without the lag that plagues dense models of similar scale.\n\nYet the MoE design introduces complexity. Routing decisions between experts must be finely tuned to avoid token-dropping or load imbalance, challenges that **Google**’s **Switch Transformer** and **Mistral**’s **Mixtral** have also grappled with. NVIDIA claims DFlash speculative decoding mitigates tail latency by predicting future tokens in parallel, but independent benchmarks on agentic tasks like **SWE-bench** or **WebArena** remain scarce at launch.\n\n## Deployment Simplicity vs. Vendor Lock-in Risks\n\nSageMaker JumpStart allows customers to deploy Nemotron 3.5 Lightning in a few clicks via the **SageMaker** console or **Python SDK**. This one-click experience lowers the barrier for teams lacking deep MLOps expertise, but it also tethers the model to AWS’s ecosystem. While the open license permits migration, the operational convenience of JumpStart’s managed endpoints and monitoring could create soft lock-in, a pattern seen with **Azure**’s **OpenAI** Service and **Google Cloud**’s **Vertex AI**.\n\nFor enterprises already invested in AWS, the integration is seamless. For others, the model’s availability on **NVIDIA NIM** and direct downloadable weights provides an escape hatch. This dual distribution strategy mirrors **Meta**’s approach with **Llama**, which is simultaneously offered through cloud marketplaces and self-hosted options.\n\nCustomers can find the model in the SageMaker JumpStart model catalog. For deployment details, see the [Amazon SageMaker JumpStart documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models.html).\n\nThe move intensifies competition in the small-but-mighty model segment. **Microsoft**’s **Phi-4**, **Anthropic**’s **Claude Haiku**, and **Google**’s **Gemma 3** all vie for enterprise agent workloads, each trading off parameter count, context window, and licensing. Nemotron 3.5 Lightning’s **1M**-token context and **410** tokens-per-second throughput set a high bar, but real-world agent reliability will ultimately determine adoption.", "url": "https://wpnews.pro/news/nvidia-nemotron-3-5-lightning-now-available-on-amazon-sagemaker-jumpstart", "canonical_source": "https://insideai.news/news/agentic-ai/nvidia-nemotron-3-5-lightning-now-available-on-amazon-sagemaker-jumpstart/7630/", "published_at": "2026-08-11 17:19:47+00:00", "updated_at": "2026-08-11 17:23:06.628668+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-products", "ai-infrastructure"], "entities": ["NVIDIA", "Nemotron 3.5 Lightning", "Amazon SageMaker JumpStart", "AWS", "DFlash", "Nemotron 3 Ultra", "Google", "Mistral"], "alternates": {"html": "https://wpnews.pro/news/nvidia-nemotron-3-5-lightning-now-available-on-amazon-sagemaker-jumpstart", "markdown": "https://wpnews.pro/news/nvidia-nemotron-3-5-lightning-now-available-on-amazon-sagemaker-jumpstart.md", "text": "https://wpnews.pro/news/nvidia-nemotron-3-5-lightning-now-available-on-amazon-sagemaker-jumpstart.txt", "jsonld": "https://wpnews.pro/news/nvidia-nemotron-3-5-lightning-now-available-on-amazon-sagemaker-jumpstart.jsonld"}}