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.
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.
The following table compares the specifications of the two variants available in JumpStart.
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.
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.
Source link
