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    Nvidia's Nemotron 3.5 Super VL goes live on Together AI

    Nvidia says the open model combines reasoning and tool use with text, image, document and video understanding.

    Together AITA
    NVIDIA AINA
    River AIRA
    32 Sources, 10h ago, first seen 10h ago

    TLDR

    Together AI says Nvidia's open-weight multimodal model is now available on its platform. Nvidia describes it as designed for long-running agents that work with text, images, documents and video, with 120 billion total parameters and 12 billion active. Nvidia says combining text and visual understanding reduces the need to pass context between separate models.

    Combined views

    169.5K

    32 Sources, first seen 10h ago

    2.4K likes140 comments511 saves260 reposts

    Combined views

    169.5K

    32 Sources, first seen 10h ago

    2.4K likes140 comments511 saves260 reposts
    Image: @testingcatalog on X

    Sentiment

    Positive85.7%14.3%Negative

    Summary

    Many accounts welcomed NVIDIA's Nemotron 3.5 Super VL for its open weights, reasoning, and long-running tool use suited to reliable agents, while some replies called the model faulty, a flop, or harmful to jobs.

    Based on 93 sentiment-bearing replies from 91 accounts across 3 conversations.

    Featured Source

    Sentiment

    Positive85.7%14.3%Negative

    Summary

    Many accounts welcomed NVIDIA's Nemotron 3.5 Super VL for its open weights, reasoning, and long-running tool use suited to reliable agents, while some replies called the model faulty, a flop, or harmful to jobs.

    Based on 93 sentiment-bearing replies from 91 accounts across 3 conversations.

    32 Sources

    🚨 AI News | TestingCatalog@testingcatalogDAILY AI BRIEF 🗞 — Oct 15 SPACEXSI 🔥: - SpaceXSI launched Grok Voice 3.0, a speech-to-speech model built for customer calls that it says responds in under a second with 40% lower latency. - The Grok Bot marketplace is now open for developer submissions of connectors, plugins and MCP servers. ANTHROPIC 🔥: - Claude Code Desktop added a sessions hub for starting, tracking and following up on multiple sessions from one screen. - Claude Managed Agents, the Messages API and the Claude SDK now support OpenTelemetry traces. - Anthropic opened a Singapore office, its fifth in Asia-Pacific, and signed an AI safety MOU with Singapore's government. PERPLEXITY 🔥: - Perplexity Computer added Ultrafast versions of GPT-6 Astra and SOL 6.1 for Max subscribers. MISTRAL 🔥: - New Material, a design and AI product studio, is joining Mistral to help build its frontier solutions. MICROSOFT 🔥: - GitHub Copilot updated HydraFusion's model pool and added a new HydraFusion Max option, both still in research preview. GOOGLE 🔥: - Google Messages is adding an enhanced protection mode against phone scams. - YouTube Music is rolling out labels for AI-generated songs. OPENAI 🔥: - Codex got another global usage-limit reset on Day 10. NVIDIA 🔥: - NVIDIA AI teased its own model launch coming next week. * Used Grok to compose this brief, cherry-picking the news and doing some post-editing.10h
    Qubax AI@Qubax_Ai🔊 3 AI moves you missed today • Gemini 4 Argon: Built full 3D ray-tracer in pure Python • Grok Voice 3.0: Leads speech benchmarks, acts as receptionist • NVIDIA: New model coming soon What model are you using most this week?4h
    Together AI@togethercompute.@NVIDIA Nemotron 3.5 Super VL is now live on Together AI. An open-weight multimodal model built for visual reasoning and always-on agents that see, reason, and act across documents, screens, and video.1h
    NVIDIA AI@NVIDIAAIAgents may need to read documents, interpret charts, and search the web to complete a task. Nemotron 3.5 Super VL brings text and visual understanding into one model, reducing the need to pass context between separate text and vision models.1h
    River AI@river_ai_incWe've partnered with @NVIDIAAI to make Nemotron 3.5 Super VL available on the River API from day zero. Even before training, Super VL outperforms Kimi K2.6 on our text-to-SQL evaluation, despite being a fraction of its size. After training with our text-to-SQL recipe on River, it outperformed every proprietary model we evaluated. These results show what's possible with River: frontier-level performance on a specialized task, using a model small enough to deploy on your own hardware.1h
    NVIDIA@nvidiaToday, we’re expanding our Nemotron family of open models with Nemotron 3.5 Super VL. Bringing visual understanding, reasoning, and tool use together to help AI agents complete more work in less time. Open models ➡️ more control to customize, deploy, and build on your own terms. Dive into what’s new:1h
    Applied Compute@appliedcomputeNemotron 3.5 Super VL by @NVIDIAAI is now supported for both training and inference on AC2. In early post-training runs for our customer, we've been impressed with the quality of the base model for its inference efficiency.1h
    Prime Intellect@PrimeIntellectTogether with NVIDIA we post-trained Nemotron 3.5 Super VL on thousands of tasks Reflecting enterprise workflows in sales, operations, marketing, finance We improved the AutomationBench-AA score by ~70% on the public split1h
    Yash Patil@ypatil125RT @appliedcompute: Nemotron 3.5 Super VL by @NVIDIAAI is now supported for both training and inference on AC2. In early post-training ru…1h
    Aleksa Gordić (水平问题)@gordic_aleksaVery excited to announce our new models: Nemotron 3.5 Super and Lightning! This also feels like a good time to share that a few months ago I joined the amazing Nemotron Pretraining team. We're cooking some very large and very strong models for you! Here is a tech summary for 3.5: * Super is a 120B total / 12B active vision language model (VLM) * Lightning is a 30B total / 3B active text-only LLM * Super scores 51.78% on SWE-bench Pro (better than Nemotron 3 Ultra despite ~4.6× fewer active params!) * arch: MoE Hybrid Mamba-Transformer designed for efficient inference, with a 1M token ctx window, and native speculative decoding (via MTP) * training is done across our GB200 and GB300 (and deployment validated across all imaginable SKUs, including consumer (RTX) and DGX Spark) A few details on how we train these: -- Pretraining * pipeline: initialize with Nemotron 3 base -> custom MTP initialization + 100B-token MTP warm-up -> 1T-token distillation from the next larger N3 teacher * Lightning then goes through a 21B token long-context (LC) stage (@ 1M ctx) * Super goes through: - Indic CPT: 100B tokens at 8K (10B Indic + 90B replay, we built a synthetic corpus with 2.83T toks across 22 Indian langs! Indic benchmarks improve by up to 9.82%) - LC extension: 35B tokens at 1M - Agentic SWE mid-training: 65.4B tokens at 256K (uses whole repos, issue-PR pairs, commit histories, etc.); DeepSWE pass@1 improves from 6.4 -> 10.4 - LC recovery: 9B tokens at 1M (as we observed regression after mid-training) -- Post-training * Super: two-stage SFT -> joint RLVR/RLHF -> specialist teachers -> two-stage MOPD -> MTP boosting * Lightning uses a lighter text-only pipeline: single-stage SFT -> RLVR * Super's SFT transforms the text-only base into a VLM using vision, agentic, reasoning, chat, safety, LC data * A few examples of RLVR environments: - Olympiad-level competitive programming - Finance agents using SEC search, document parsing, evidence retrieval, and tool use * Specialist teachers include Agentic, Reasoning, Video reasoning and tracking, Vision tool-use, Vision-reasoning, and an Alignment/RLHF teacher * Two-stage MOPD consolidates these specialist capabilities into one model * Post-training ends with MTP boosting to better align the drafter and reduce recursive train–inference mismatch; this improves Super's SPEED-Bench acceptance length by 21.8% (to 4.612 tokens) -- Inference The model is highly optimized for inference (with plenty of quantized options to choose from)! A few data points: * On a GB200, Super reaches 4,856 output toks/s/GPU with ≥50 toks/s/user on a 20-turn agentic workload * Super provides 2.34x the throughput of Qwen3.5 122B-A10B and 1.90x the throughput of Qwen3.8 Flash-Next * Native MTP is complemented by standalone parallel drafters: LiLiCorr for Super, and DFlash and DSpark for Lightning Looking forward to seeing what you do with 3.5! Phase shift for Nemotron incoming! :)) (with the usual this will be our weakest model ever, but really really so) paper: https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3.5-Super-and-Lightning-Technical-Report.pdf1h
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    32 Sources

    🚨 AI News | TestingCatalog@testingcatalogDAILY AI BRIEF 🗞 — Oct 15 SPACEXSI 🔥: - SpaceXSI launched Grok Voice 3.0, a speech-to-speech model built for customer calls that it says responds in under a second with 40% lower latency. - The Grok Bot marketplace is now open for developer submissions of connectors, plugins and MCP servers. ANTHROPIC 🔥: - Claude Code Desktop added a sessions hub for starting, tracking and following up on multiple sessions from one screen. - Claude Managed Agents, the Messages API and the Claude SDK now support OpenTelemetry traces. - Anthropic opened a Singapore office, its fifth in Asia-Pacific, and signed an AI safety MOU with Singapore's government. PERPLEXITY 🔥: - Perplexity Computer added Ultrafast versions of GPT-6 Astra and SOL 6.1 for Max subscribers. MISTRAL 🔥: - New Material, a design and AI product studio, is joining Mistral to help build its frontier solutions. MICROSOFT 🔥: - GitHub Copilot updated HydraFusion's model pool and added a new HydraFusion Max option, both still in research preview. GOOGLE 🔥: - Google Messages is adding an enhanced protection mode against phone scams. - YouTube Music is rolling out labels for AI-generated songs. OPENAI 🔥: - Codex got another global usage-limit reset on Day 10. NVIDIA 🔥: - NVIDIA AI teased its own model launch coming next week. * Used Grok to compose this brief, cherry-picking the news and doing some post-editing.10h
    Qubax AI@Qubax_Ai🔊 3 AI moves you missed today • Gemini 4 Argon: Built full 3D ray-tracer in pure Python • Grok Voice 3.0: Leads speech benchmarks, acts as receptionist • NVIDIA: New model coming soon What model are you using most this week?4h
    Together AI@togethercompute.@NVIDIA Nemotron 3.5 Super VL is now live on Together AI. An open-weight multimodal model built for visual reasoning and always-on agents that see, reason, and act across documents, screens, and video.1h
    NVIDIA AI@NVIDIAAIAgents may need to read documents, interpret charts, and search the web to complete a task. Nemotron 3.5 Super VL brings text and visual understanding into one model, reducing the need to pass context between separate text and vision models.1h
    River AI@river_ai_incWe've partnered with @NVIDIAAI to make Nemotron 3.5 Super VL available on the River API from day zero. Even before training, Super VL outperforms Kimi K2.6 on our text-to-SQL evaluation, despite being a fraction of its size. After training with our text-to-SQL recipe on River, it outperformed every proprietary model we evaluated. These results show what's possible with River: frontier-level performance on a specialized task, using a model small enough to deploy on your own hardware.1h
    NVIDIA@nvidiaToday, we’re expanding our Nemotron family of open models with Nemotron 3.5 Super VL. Bringing visual understanding, reasoning, and tool use together to help AI agents complete more work in less time. Open models ➡️ more control to customize, deploy, and build on your own terms. Dive into what’s new:1h
    Applied Compute@appliedcomputeNemotron 3.5 Super VL by @NVIDIAAI is now supported for both training and inference on AC2. In early post-training runs for our customer, we've been impressed with the quality of the base model for its inference efficiency.1h
    Prime Intellect@PrimeIntellectTogether with NVIDIA we post-trained Nemotron 3.5 Super VL on thousands of tasks Reflecting enterprise workflows in sales, operations, marketing, finance We improved the AutomationBench-AA score by ~70% on the public split1h
    Yash Patil@ypatil125RT @appliedcompute: Nemotron 3.5 Super VL by @NVIDIAAI is now supported for both training and inference on AC2. In early post-training ru…1h
    Aleksa Gordić (水平问题)@gordic_aleksaVery excited to announce our new models: Nemotron 3.5 Super and Lightning! This also feels like a good time to share that a few months ago I joined the amazing Nemotron Pretraining team. We're cooking some very large and very strong models for you! Here is a tech summary for 3.5: * Super is a 120B total / 12B active vision language model (VLM) * Lightning is a 30B total / 3B active text-only LLM * Super scores 51.78% on SWE-bench Pro (better than Nemotron 3 Ultra despite ~4.6× fewer active params!) * arch: MoE Hybrid Mamba-Transformer designed for efficient inference, with a 1M token ctx window, and native speculative decoding (via MTP) * training is done across our GB200 and GB300 (and deployment validated across all imaginable SKUs, including consumer (RTX) and DGX Spark) A few details on how we train these: -- Pretraining * pipeline: initialize with Nemotron 3 base -> custom MTP initialization + 100B-token MTP warm-up -> 1T-token distillation from the next larger N3 teacher * Lightning then goes through a 21B token long-context (LC) stage (@ 1M ctx) * Super goes through: - Indic CPT: 100B tokens at 8K (10B Indic + 90B replay, we built a synthetic corpus with 2.83T toks across 22 Indian langs! Indic benchmarks improve by up to 9.82%) - LC extension: 35B tokens at 1M - Agentic SWE mid-training: 65.4B tokens at 256K (uses whole repos, issue-PR pairs, commit histories, etc.); DeepSWE pass@1 improves from 6.4 -> 10.4 - LC recovery: 9B tokens at 1M (as we observed regression after mid-training) -- Post-training * Super: two-stage SFT -> joint RLVR/RLHF -> specialist teachers -> two-stage MOPD -> MTP boosting * Lightning uses a lighter text-only pipeline: single-stage SFT -> RLVR * Super's SFT transforms the text-only base into a VLM using vision, agentic, reasoning, chat, safety, LC data * A few examples of RLVR environments: - Olympiad-level competitive programming - Finance agents using SEC search, document parsing, evidence retrieval, and tool use * Specialist teachers include Agentic, Reasoning, Video reasoning and tracking, Vision tool-use, Vision-reasoning, and an Alignment/RLHF teacher * Two-stage MOPD consolidates these specialist capabilities into one model * Post-training ends with MTP boosting to better align the drafter and reduce recursive train–inference mismatch; this improves Super's SPEED-Bench acceptance length by 21.8% (to 4.612 tokens) -- Inference The model is highly optimized for inference (with plenty of quantized options to choose from)! A few data points: * On a GB200, Super reaches 4,856 output toks/s/GPU with ≥50 toks/s/user on a 20-turn agentic workload * Super provides 2.34x the throughput of Qwen3.5 122B-A10B and 1.90x the throughput of Qwen3.8 Flash-Next * Native MTP is complemented by standalone parallel drafters: LiLiCorr for Super, and DFlash and DSpark for Lightning Looking forward to seeing what you do with 3.5! Phase shift for Nemotron incoming! :)) (with the usual this will be our weakest model ever, but really really so) paper: https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3.5-Super-and-Lightning-Technical-Report.pdf1h
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