Always-on enterprise agents keep taking on longer workflows: a single request may read a multi-page document, cross-check sources, follow company policy, and call several tools. That used to mean a text model plus a separate vision-language model, passing context back and forth. On October 15, NVIDIA open-sourced Nemotron 3.5 Super VL and collapsed the relay into one runner: a 120B-parameter multimodal mixture-of-experts model that packs reasoning, instruction following, tool use, and text, image, document, and video understanding into a single checkpoint (NVIDIA Technical Blog).
One model, two stacks' worth of work
Super VL's visual training covers charts, tables, OCR, multipage documents, and video. NVIDIA's framing: in document intelligence, the same model can interpret a chart, connect it with a table elsewhere in the document, and summarize both. On CharXiv, a chart-reasoning benchmark, it scores 80.8% versus 63% for the previous Nemotron 3 Nano Omni, and 84% when paired with Python code interpretation. It also ranks among the top open models on the OCRBench v2 leaderboard. For teams building agents, that removes the need to deploy and maintain a separate vision model.
120B of capacity, a 12B bill
Architecturally, Super VL is a MoE with 120B total parameters and only 12B active per token. It was distilled from the larger Nemotron 3 Ultra using the SkewReverse loss, with multi-teacher on-policy distillation (MOPD) in post-training where text and vision teachers score generated responses. According to Modal's model card, it uses a hybrid Mamba-Transformer architecture with the C-RADIOv4 vision encoder, supports up to 1 million tokens of context, and has built-in multi-token prediction. NVIDIA's efficiency claims are aggressive: up to 5x higher throughput for agentic tasks on a single GPU versus other open models in its size category, over 600 output tokens per second on the Baseten endpoint running Artificial Analysis Intelligence Index tasks, and 4x faster completion of 10,000 coding tasks.
Four tricks on the inference side
What really determines the cost of an always-on agent is inference. Super VL ships with four pieces: LiLiCorr, an open speculative-decoding technique that jointly evaluates token compatibility across an entire draft, delivering up to 2.3x faster generation on DGX Spark; NVFP4 quantization with quantization-aware distillation (QAD), where the quantized model learns to match the BF16 original during training and the KV cache can also be stored in NVFP4 with no inference overhead; multi-token prediction (MTP), verifying several candidate tokens per forward pass; and context reuse across agent turns that preserves both attention caches and Mamba recurrent states. On the serving side, NVIDIA Dynamo's KV-aware routing serves up to 8x as many users on 16 B200 GPUs at 50 tokens per second per user, with 85% lower first-token latency.
The enterprise post-training ledger
Open weights matter because of post-training. Scale AI post-trained the model for automated vendor credit processing at Skydance; NVIDIA projects 40x lower inference cost and 5x lower latency. fintech platform Ramp converts receipt images into structured JSON and expects up to 50% higher throughput with 10x lower latency. AI21 and Alice post-trained it for threat intelligence and vulnerability analysis, improving accuracy by 41%. NVIDIA's shown domain gains range from +15% on document intelligence to +44% on medical insurance claims. On benchmarks, it scores 63.44% on Hermes Bench and 87.05% on LangChain evals, beating the larger Nemotron 3 Ultra on both.
The full open puzzle — and the business logic
This release is more than a dropped checkpoint: alongside the weights, NVIDIA open-sourced datasets for agentic tool use, finance reasoning, and long-context work (including more than 40 vision-language datasets), plus a 2.8T-token Indian-language dataset built with IIT Madras's AI4Bharat lab, after injecting a 10B-token, 12-language subset during training. Two companion models target the edge: Bolt 74B-A9B, for local machines with 24GB of memory, is available now, while the 4GB Bolt E4B for voice assistants follows on October 23 (The Decoder). The Decoder's observation is worth remembering: the model is fully open, but the whole optimization chain is tuned for NVIDIA's own hardware — squeeze the performance and you end up buying its chips.
So what? The agent-infrastructure race is shifting from "whose model scores highest" to "who can run an always-on agent cheaply on a single GPU." NVIDIA pairs open weights with datasets, recipes, and deployment tooling, lowering the barrier until any team can fork and start — at the cost of walking into its hardware stack by default. For developers, that's a good deal; for rival clouds, it's textbook ecosystem positioning.