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On October 15, 2026, Nvidia announced the launch of Nemotron 3.5 Super VL and the edge-oriented 3.5 Bolt models. According to Nvidia's official introduction, Super VL is a 120B-parameter open model aimed at always-on agent tasks, and the company says it has the highest throughput among comparable open models, 5 times faster than the next-best model; it also launched two edge models, Bolt 74B-A9B 24GB and Bolt E4B 4GB, with the former able to run agent tasks locally on RTX/DGX Spark and the latter aimed at voice and conversational agents on GeForce RTX and Jetson. Super VL and Bolt 74B-A9B are already available on Hugging Face, and Bolt E4B is scheduled for release on October 23. On the same day, SGLang announced Day-0 support for Super VL, reaching 243.9 tok/s on a single RTX 6000 PRO with the help of MTP; Prime Intellect and Nvidia used GRPO to post-train it for about 11 hours on 128 GB200 GPUs, raising its score on the public AutomationBench-AA set from 34.0% to 57.7%. That evening OpenRouter listed the model, and the next day Perplexity announced that Pro and Max subscribers and the Agent API can use it.
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- Source:Perplexity
- Source:NVIDIA:Newsroom
- Source:LMSYS Blog
- Source:Prime Intellect
- Source:OpenRouter
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Oct 18
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Oct 17
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Oct 16
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The New Stack
Running local AI models on the Microsoft Surface Laptop Ultra: hands-on findings
The New Stack tested running local AI models on the Microsoft Surface Laptop Ultra (Nvidia N1X platform, Blackwell RTX GPU, 64GB of memory, $4,300 configuration): Nemotron 3.5 Bolt reached 56-60 tokens/s, while 8-bit Qwen 3.8 27B managed only 8-9 tokens/s, recovering to 25-28 tokens/s after dropping to 4-bit. On battery, AI workload performance dropped by about 20%-25%, inconsistent with Microsoft's claim of retaining 99.6%.
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Analytics India Magazine
NVIDIA Expands Nemotron Model Family with New Super VL and Two Bolt Models
NVIDIA has added three models to the Nemotron open-source model family, covering enterprise workflows, local AI agents, and conversational applications across data centers, desktops, and edge devices. These include the 120-billion-parameter Nemotron 3.5 Super VL, which NVIDIA claims delivers up to 5x the throughput of the next-best comparable model, with accuracy approaching that of its largest reasoning model at roughly one-fifth the parameter count; there are also two small models in the Nemotron 3.5 Bolt series: 74B-A9B 24GB for everyday agent tasks on RTX and DGX Spark, and E4B 4GB for conversational and physical AI agents. Super VL and Bolt 74B-A9B are already available, and Bolt E4B is scheduled for release on October 23.
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Artificial Analysis full articleFirst-partySelected
Reflection Releases Its First Model Beam, Scoring 30 on the Artificial Analysis Intelligence Index
Reflection has released its first model, Beam, an open-weight model with 501B total parameters and 23B activated parameters. It scores 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). Beam consumes about 26k output tokens per Intelligence Index task, of which about 19k are used for reasoning and 7k for answering; it scores 48.5% on AutomationBench-AA, but only 3.0% on Terminal-Bench 4.0. On AA-Omniscience, it answers 33% of questions correctly, with a hallucination rate of 47% and a net index score of 1.5.
marktechpost
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Perplexity
Perplexity Launches NVIDIA Nemotron 3.5 Super VL
Perplexity announced that NVIDIA's Nemotron 3.5 Super VL is now available on its platform, accessible to Pro and Max subscribers as well as via the Agent API. The original article did not disclose the model's specific capability parameters or pricing.
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Oct 15
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OpenRouterFirst-party
NVIDIA Nemotron 3.5 Super VL Launches on OpenRouter
NVIDIA Nemotron 3.5 Super VL is now available on OpenRouter. It is an open-source 120B-A12B multimodal model designed for long-running agents that need to observe, reason, and act across text, images, documents, GUIs, and video, with support for a 1M context. The model uses a Hybrid Mamba-Transformer MoE architecture and supports multi-token prediction to speed up output, targeting scenarios such as document intelligence, web research, customer service, and video understanding.
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