# NVIDIA Nemotron 3.5 Super VL is now available in Amazon SageMaker JumpStart > Source: > Published: 2026-11-09 20:01:20+00:00 ## [Artificial Intelligence](https://aws.amazon.com/blogs/machine-learning/) # NVIDIA Nemotron 3.5 Super VL is now available in Amazon SageMaker JumpStart Today, we’re announcing that NVIDIA Nemotron 3.5 Super VL is available in Amazon SageMaker JumpStart. You can deploy it with a few steps in SageMaker Studio or with a few lines of the SageMaker Python SDK. Super VL is the vision-language model (VLM) in the NVIDIA Nemotron 3.5 family. It accepts images, video, and text as input and generates text. The model pairs a 120B-parameter hybrid Mixture-of-Experts (MoE) language backbone with 12B active parameters and a C-RADIO vision encoder. It supports context lengths of up to 1 million tokens and includes a built-in Multi-Token Prediction (MTP) head that supports speculative decoding. You can configure reasoning per request. NVIDIA built Super VL for document intelligence, GUI understanding for computer-use agents, video question answering, and multimodal agentic applications. JumpStart offers two variants: NVFP4, quantized from BF16 with quantization-aware distillation for optimized inference, and the BF16 full-precision reference weights. In this post, we walk through the model’s capabilities and the JumpStart deployment configurations. We then show the measured inference performance and demonstrate text, image, and video inference. ## Model overview Nemotron 3.5 Super VL is a 120B-parameter model with 12B active parameters per token. It combines a hybrid language backbone with a dedicated vision encoder. - **Language backbone** : 88 layers that interleave Mamba-2 and MoE layers (512 routed experts, 22 active per token) with 8 attention layers, plus a shared MTP head. The backbone is initialized from Nemotron 3 Super Base and distilled from Nemotron 3 Ultra Base. - **Vision encoder** : A C-RADIO vision encoder encodes images and sampled video frames, and a vision-language connector maps them into the language backbone’s embedding space. The encoder and connector stay in BF16 in both variants. - **Training** : The model first goes through continued pre-training, long-context and software-engineering mid-training, and vision-language training. It then goes through two-stage multimodal supervised fine-tuning (SFT) on vision, reasoning, code, tool, chat, safety, and long-context data. Next come reinforcement learning (RL) with verifiable rewards and human feedback in NeMo RL and NeMo Gym, multi-teacher on-policy distillation, and a final stage that further trains the MTP head. The NVFP4 checkpoint is produced from BF16 with NVIDIA Model Optimizer and quantization-aware distillation. It keeps the vision encoder, connector, Mamba convolutions, and embeddings in BF16 and the MoE router in FP32. It runs with 4-bit weights and activations (W4A4) on Blackwell GPUs. The following table compares the specifications of the two variants available in JumpStart. | **Specification** | **NVFP4** | **BF16** | | Parameters | 120B total, 12B active | 120B total, 12B active | | Intended for | Optimized inference (latency and throughput) | Full-precision reference weights for customization: SFT, RL, distillation, domain adaptation, and custom quantization | | Inputs / output | Image, video, text / text | Image, video, text / text | | Context length | Up to 1M tokens (JumpStart serving configurations use 520 to 15,600 tokens) | Same | | Checkpoint size | About 75 GB | About 249 GB | | JumpStart instances | ml.g7.24xlarge (default), ml.g7.48xlarge | ml.p5en.48xlarge | | Speculative decoding | Built-in MTP head. Not enabled in the JumpStart serving configuration | Same | | LoRA adapter serving | No | No | | Languages | English, French, German, Italian, Japanese, Spanish, Chinese | Same | | Reasoning | On by default. Configurable with `enable_thinking` | Same | | Outputs | Tool calling, JSON schema output, configurable reasoning | Same | | License | [OpenMDW License Agreement, version 1.1](https://openmdw.ai/license/1-1/) | Same | ## Key use cases Super VL is aimed at agentic workloads that must see and read. It targets vision-language agents that combine reasoning, coding, tool use, document understanding, and video understanding. - **Document intelligence** : Optical character recognition (OCR), charts, tables, and multipage documents. Each 1,536 x 1,024 page image costs about 1,536 tokens of context. The JumpStart serving configuration accepts one image and one video per request. - **GUI understanding** : Screen grounding and reasoning for computer-use agents that navigate applications. - **Video understanding** : Analyze and summarize short MP4 video clips, including answering questions about visual content. You can provide videos inline as base64-encoded content, subject to the endpoint’s request payload size limits. - **Multimodal agents** : Tool calling, JSON schema output through`response_format` , and configurable reasoning support multimodal agent workflows. - **Customization (BF16)** : Download the BF16 reference weights for SFT, RL, distillation, domain adaptation, or creating your own quantized variants. The JumpStart endpoint serves the base weights only and does not load LoRA adapters. ## Model-quality benchmarks The NVIDIA model cards report results for both checkpoints across reasoning, coding, agentic, long-context, and multimodal benchmarks, with evaluation settings held constant so that only the checkpoint differs. The following table highlights the multimodal benchmarks most relevant to vision-language workloads (reasoning on). | **Benchmark** | **Capability** | **BF16** | **NVFP4** | | MMLongBench-Doc | Long-document multimodal understanding | 62.82 | 62.82 | | OCRBench v2, EN | OCR and document understanding | 68.12 | 68.34 | | CharXiv reasoning | Chart and figure reasoning | 80.82 | 79.88 | | Video-MME v2 | Video understanding | 32.85 | 32.54 | | OSWorld-Verified | Visual computer use and agentic tasks | 51.14 | 52.30 | NVFP4, the JumpStart default, matches BF16 on MMLongBench-Doc and stays within about one point on the others, scoring slightly higher on OCRBench v2 and OSWorld-Verified. For the full comparison against other publicly available models and the evaluation settings, see the [NVIDIA Nemotron 3.5 Super VL and Lightning Technical Report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3.5-Super-and-Lightning-Technical-Report.pdf). ## Inference performance on SageMaker JumpStart We measured inference performance with the JumpStart release container using the generate workload profile: 250 input tokens, 1,500 output tokens, and five concurrent streaming requests. Throughput is the mean output tokens per second for each request, and time to first token is the mean across requests. | **Instance type** | **Variant** | **Output throughput per request (mean)** | **Time to first token (mean)** | | ml.g7.24xlarge | NVFP4 | 63.07 tokens/sec | 0.45 sec | | ml.g7.48xlarge | NVFP4 | 64.01 tokens/sec | 0.51 sec | | ml.p5en.48xlarge | BF16 | 140.85 tokens/sec | 0.53 sec | These results illustrate the deployment choices available through JumpStart. The NVFP4 variant runs on G7 instances with a smaller GPU footprint. The BF16 variant on ml.p5en.48xlarge achieved 140.85 output tokens per second per request in this test. Time to first token remained about half a second across all three configurations. Because the variants run on different GPU systems and configurations, these measurements are not an isolated `BF16-versus-NVFP4` precision comparison. Longer prompts change the picture: with the summarize profile (10,000 input tokens), time to first token on ml.g7.24xlarge is 12.62 seconds. ## Supported instances SageMaker JumpStart supports Nemotron 3.5 Super VL on the following validated instance types. NVFP4 runs on G7 instances with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, and BF16 runs on P5en instances with NVIDIA H200 GPUs. The JumpStart default instance for NVFP4 is ml.g7.24xlarge, which serves as the recommended starting point for the generate profile. | **Variant** | **Instance type** | **GPUs** | **GPU memory** | **Tensor parallel** | | NVFP4 | ml.g7.24xlarge (default) | 4x NVIDIA RTX PRO 4500 Blackwell Server Edition | 128 GB | 4 | | NVFP4 | ml.g7.48xlarge | 8x NVIDIA RTX PRO 4500 Blackwell Server Edition | 256 GB | 8 | | BF16 | ml.p5en.48xlarge | 8x NVIDIA H200 | 1,128 GB | 8 | The checkpoint includes the MTP head, but the JumpStart serving configuration does not enable speculative decoding. ## Deploy Nemotron 3.5 Super VL with SageMaker JumpStart You can deploy the model from SageMaker Studio, with the SageMaker Python SDK, or from the Hugging Face model page. ### Prerequisites Before you deploy, make sure you have the following: - An AWS account with permissions to create Amazon SageMaker AI endpoints. - An AWS Identity and Access Management (IAM) role with access to SageMaker AI. - Service quota for ml.g7.24xlarge, ml.g7.48xlarge, or ml.p5en.48xlarge endpoint usage. The ml.g7 quotas start at zero in new accounts, so request an increase through Service Quotas before you deploy. - For the SDK path, the SageMaker Python SDK v3 (version 3.25 or later). ### Deploy from SageMaker Studio To deploy the model from SageMaker Studio, complete the following steps: 1. Open SageMaker Studio and, in the navigation pane, choose **JumpStart** . 2. In the search bar, enter Nemotron 3.5 Super VL. The search results list both variants, as shown in the following screenshot. 3. Choose the model card for the variant you want to deploy. Choose NVFP4 for optimized inference on G7 instances. The following screenshot shows the NVFP4 model card. Choose BF16 if you need the full-precision reference weights on ml.p5en.48xlarge. The following screenshot shows the BF16 model card. 4. On the model card, review the model details and license terms, then choose **Deploy** . The following screenshot shows the model details you can review before deployment. 5. For **Instance type** , keep the default (ml.g7.24xlarge for NVFP4 or ml.p5en.48xlarge for BF16), or choose another supported instance type. Then choose**Deploy** . 6. Wait for the endpoint status to change to InService before you invoke the model. Deployment takes about 10 minutes on ml.g7.24xlarge. The following screenshot shows a deployment in progress.