{"id":583,"date":"2026-10-20T08:22:48","date_gmt":"2026-10-20T00:22:48","guid":{"rendered":"https:\/\/aidashxp.com\/nvidia-nemotron-3-5-super-vl-review\/"},"modified":"2026-10-20T08:22:48","modified_gmt":"2026-10-20T00:22:48","slug":"nvidia-nemotron-3-5-super-vl-review","status":"publish","type":"post","link":"https:\/\/aidashxp.com\/en\/nvidia-nemotron-3-5-super-vl-review\/","title":{"rendered":"NVIDIA Nemotron 3.5 Super VL in-depth review: a 120B open-source agent model, a new option for enterprise private deployment"},"content":{"rendered":"<p class=\"wp-block-paragraph\">NVIDIA has placed another heavy bet on the open-source large model\u8d5b\u9053. In mid-October, NVIDIA officially launched <strong>Nemotron 3.5<\/strong> the Nemotron 3.5 series, unveiling three open-weight models at once\u2014<strong>Super VL\uff08120B\uff09<\/strong>Nemotron 3.5 Super VL, targeting enterprise-grade Agents,<strong>Bolt 74B-A9B<\/strong>Nemotron 3.5 Edge, aimed at local AI PCs, and the smallest, <strong>Bolt E4B<\/strong>Nemotron 3.5 Chat, dedicated to lightweight dialogue. This marks NVIDIA\u2019s most comprehensive product lineup yet in the open-source Agent space, following <a href=\"https:\/\/aidashxp.com\/en\/nvidia-nemotron-3-ultra-review\/\">Nemotron 3 Ultra<\/a> Nemotron 3 Ultra. Its core value proposition boils down to one sentence:<strong>Enable organizations to run their own agents at any compute tier\u2014no trade-off between \u201ccapability\u201d and \u201cdeployability.\u201d<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Core capabilities: Three models spanning enterprise to individual use<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The biggest shift in the Nemotron 3.5 series is moving away from the previous \u201cone flagship dominates all\u201d approach to three clearly differentiated lines.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Nemotron 3.5 Super VL\uff08120B\uff09<\/strong>Nemotron 3.5 Super VL is this round\u2019s flagship, designed for enterprise scenarios requiring \u201calways-on\u201d Agents. Though its parameter count is significantly smaller than the 550B Nemotron 3 Ultra, NVIDIA claims it delivers comparable accuracy in<strong>instruction following and tool calling,<\/strong>two critical capabilities. Official target use cases include document intelligence, financial accounting, scientific experimentation, health insurance claims processing, and security offensive\/defensive resilience\u2014precisely the tasks enterprises are most willing to pay for under strict \u201cdata stays within domain\u201d requirements. NVIDIA also cites a roster of heavyweight customer endorsements: Skydance, EY, Ramp, CrowdStrike,<a href=\"https:\/\/perplexity.ai\" target=\"_blank\" rel=\"nofollow noopener\">Perplexity<\/a> and Salesforce\u2014all deploying Super VL.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Nemotron 3.5 Bolt 74B-A9B<\/strong>represents the \u201clocal-first\u201d approach. It is a 74B-parameter Mixture of Experts (MoE) model with approximately 9B activated parameters, specifically designed for local agentic tasks, aiming to<strong>strike a balance between speed and reasoning<\/strong>and handle multi-step reasoning, tool calling, and web browsing\u2014workflows requiring sequential decision-making. It runs on next-generation AI PCs equipped with <a href=\"https:\/\/aidashxp.com\/en\/nvidia-rtx-spark-ai-pc\/\">NVIDIA RTX Spark chips<\/a>and DGX Spark workstations\u2014devices now being rolled out at scale by partners including Microsoft and Dell, targeting consumers, programmers, and enterprise knowledge workers.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The smallest <strong>Bolt E4B<\/strong>is positioned as a conversational agent, with fewer parameters and lower power consumption, suitable for lightweight voice or text interactions. Super VL and Bolt 74B-A9B are already available for download; Bolt E4B will be released later this month.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">User experience and limitations<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As an open-weight model, Nemotron 3.5\u2019s biggest appeal lies in<strong>\u201cdata sovereignty\u201d<\/strong>NVIDIA Software Vice President Kari Briski put it plainly: unlike closed frontier models, organizations can perform post-training of Super VL on their own data, deploy it on their own infrastructure, and truly \u201cown what they build.\u201d For compliance-sensitive domains such as healthcare, finance, and government, this capability is irreplaceable by closed-source APIs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">On performance, NVIDIA has released notably aggressive benchmarks: after task-specific post-training for agentic workloads, Super VL\u2019s task completion speed surpasses Anthropic\u2019s Opus 5 and OpenAI\u2019s GPT-6.1 Sol, Luna, and Astra. However, such \u201cofficial self-benchmarks\u201d are already industry-standard practice, and real-world effectiveness awaits third-party evaluations and practical deployment validation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Limitations are equally apparent:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Deployment barrier<\/strong>: Open source \u2260 plug-and-play. Super VL has 120B parameters and requires enterprise-grade multi-GPU setups for local inference; while Bolt 74B runs on AI PCs, its 24GB VRAM requirement still excludes many mainstream users.<\/li>\n<li><strong>Chinese-language capability remains questionable<\/strong>: Official materials do not emphasize Chinese-language performance; domestic users targeting Chinese writing or Chinese-language agents will likely find domestic open-source models more convenient.<\/li>\n<li><strong>Ecosystem is still maturing<\/strong>: Compared to CUDA\u2019s dominance in training, Nemotron\u2019s inference ecosystem, community tooling, and fine-tuning tutorials require further time to mature.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Nemotron 3.5 Super VL vs. peer open-source models: horizontal comparison<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead><tr><th>Model<\/th><th>Parameter count<\/th><th>Core Positioning<\/th><th>This Site\u2019s Rating<\/th><\/tr><\/thead>\n<tbody>\n<tr><td><strong>Nemotron 3.5 Super VL<\/strong><\/td><td>120B<\/td><td>Enterprise Agent On-Premises<\/td><td>8.3<\/td><\/tr>\n<tr><td><a href=\"https:\/\/aidashxp.com\/en\/nvidia-nemotron-3-ultra-review\/\">Nemotron 3 Ultra<\/a><\/td><td>550B<\/td><td>Strongest open-source model in the U.S.<\/td><td>6.8<\/td><\/tr>\n<tr><td><a href=\"https:\/\/aidashxp.com\/en\/glm-5-3-review\/\">GLM-5.3<\/a><\/td><td>Open-Source Inference<\/td><td>Programming Agent<\/td><td>8.4<\/td><\/tr>\n<tr><td><a href=\"https:\/\/aidashxp.com\/en\/deepseek-v4-pro-0813-review\/\">DeepSeek V4 Pro<\/a><\/td><td>MoE Flagship<\/td><td>Flagship at Mass-Market Pricing<\/td><td>8.9<\/td><\/tr>\n<tr><td><a href=\"https:\/\/aidashxp.com\/en\/qwen38-2-4t-a95b-review\/\">Qwen3.8 2.4T<\/a><\/td><td>2.4T<\/td><td>Tongyi Open-Source Milestone<\/td><td>8.5<\/td><\/tr>\n<tr><td><a href=\"https:\/\/aidashxp.com\/en\/minimax-m3-review\/\">MiniMax M3<\/a><\/td><td>Open Weights<\/td><td>Million-Token Programming<\/td><td>8.1<\/td><\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">From a coordinate-system perspective, Nemotron 3.5 Super VL fills NVIDIA\u2019s gap in the \u201cmid-to-large enterprise Agent\u201d niche: it is more pragmatic than its predecessor Nemotron 3 Ultra (half the parameters, easier deployment), yet <a href=\"https:\/\/aidashxp.com\/en\/deepseek-v4-pro-0813-review\/\">DeepSeek V4 Pro<\/a>,<a href=\"https:\/\/aidashxp.com\/en\/qwen38-2-4t-a95b-review\/\">Qwen3.8<\/a> Compared with these domestic open-source flagships, Chinese-language ecosystem support and community momentum remain weaknesses. If you are an overseas enterprise prioritizing data sovereignty and hardware integration, Super VL offers unique value; if you are a domestic team building Chinese-language Agents, domestic models remain the safer choice for now.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Overall Score<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead><tr><th>\u7ef4\u5ea6<\/th><th>Score<\/th><th>evaluate<\/th><\/tr><\/thead>\n<tbody>\n<tr><td>functional completeness<\/td><td>8.5 \/ 10<\/td><td>Comprehensive tool calling, multi-step reasoning, and enterprise-task coverage<\/td><\/tr>\n<tr><td>\u6613\u7528\u6027<\/td><td>7.5 \/ 10<\/td><td>High open-source deployment barrier; local execution requires multi-GPU setup<\/td><\/tr>\n<tr><td>Cost-effectiveness<\/td><td>8.5 \/ 10<\/td><td>Weights are free, but hardware and operational costs are self-managed<\/td><\/tr>\n<tr><td>\u4e2d\u6587\u652f\u6301<\/td><td>7.0 \/ 10<\/td><td>Official Chinese-language optimization is limited; domestic use-case performance is suboptimal<\/td><\/tr>\n<tr><td>\u8f93\u51fa\u8d28\u91cf<\/td><td>8.5 \/ 10<\/td><td>Post-training on agentic tasks achieves performance comparable to cutting-edge closed-source models<\/td><\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Overall rating: 8.3\/10<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions (FAQ)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Is Nemotron 3.5 Super VL free?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes\u2014the model weights are open-source and freely downloadable, and may be freely fine-tuned and used commercially. However, \u201cfree\u201d applies only to the model itself; actual operation requires self-provided GPU hardware, plus electricity and operational expenses\u2014enterprise-scale deployment of this 120B-parameter model incurs significant cost.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What distinguishes Nemotron 3.5 Super VL from Nemotron 3 Ultra?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Super VL has fewer parameters (120B vs. 550B), yet NVIDIA claims its accuracy in instruction following and tool calling is comparable; it is positioned as a more pragmatic, easier-to-deploy enterprise Agent model, whereas Ultra pursues maximum scale as a flagship. For most enterprises, Super VL delivers better cost-performance balance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What configuration is required to deploy Nemotron 3.5 locally?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Bolt 74B-A9B targets devices with 24 GB VRAM and runs on AI PCs equipped with RTX Spark chips or DGX Spark workstations; the flagship Super VL model has 120 billion parameters and requires enterprise-grade multi-GPU clusters.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does Nemotron 3.5 support Chinese? How well does it perform?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The official documentation does not emphasize Chinese-language capabilities, and its optimization for Chinese-language scenarios is likely inferior to domestic open-source models such as GLM and<a href=\"https:\/\/chat.deepseek.com\" target=\"_blank\" rel=\"nofollow noopener\">DeepSeek<\/a>Qwen. For Chinese-language writing or Chinese-language Agents, domestic users are advised to prioritize leading domestic open-source models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Nemotron 3.5 and <a href=\"https:\/\/chat.deepseek.com\" target=\"_blank\" rel=\"nofollow noopener\">DeepSeek<\/a>Qwen\u2014which should you choose?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">It depends on the use case. For enterprise Agent privatization with emphasis on data sovereignty and NVIDIA hardware integration, choose Super VL; for Chinese-language programming and Chinese-language Agents,<a href=\"https:\/\/aidashxp.com\/en\/deepseek-v4-pro-0813-review\/\">DeepSeek V4 Pro<\/a> and <a href=\"https:\/\/aidashxp.com\/en\/qwen38-2-4t-a95b-review\/\">Qwen3.8<\/a> Qwen remains the more mature choice.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Summarize<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The significance of the Nemotron 3.5 series extends beyond adding another open-source model\u2014it marks NVIDIA\u2019s transformation of \u201copen-source Agents\u201d from a slogan into a complete product matrix spanning enterprises, workstations, and personal PCs. For teams seeking private Agent solutions without vendor lock-in to a single cloud provider, this is a compelling new option to include in their evaluation list.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Want to discover more useful AI models and tools? Check out our <a href=\"https:\/\/aidashxp.com\/en\/ai-models\/\">AI Model Library<\/a> and <a href=\"https:\/\/aidashxp.com\/en\/compare-tools\/\">Tool Comparison Engine<\/a>or continue reading:<a href=\"https:\/\/aidashxp.com\/en\/nvidia-nemotron-3-ultra-review\/\">Nemotron 3 Ultra Deep Review<\/a> \u00b7 <a href=\"https:\/\/aidashxp.com\/en\/glm-5-3-review\/\">GLM-5.3 Deep Review<\/a> \u00b7 <a href=\"https:\/\/aidashxp.com\/en\/mistral-large-4-le-chonk-1t-model\/\">Mistral Large 4 Release<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>NVIDIA \u5728\u5f00\u6e90\u5927\u6a21\u578b\u8d5b\u9053\u4e0a\u53c8\u4e0b\u4e86\u4e00 [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[6],"tags":[],"class_list":["post-583","post","type-post","status-publish","format-standard","hentry","category-ai-coding"],"_links":{"self":[{"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/posts\/583","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/comments?post=583"}],"version-history":[{"count":0,"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/posts\/583\/revisions"}],"wp:attachment":[{"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/media?parent=583"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/categories?post=583"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/tags?post=583"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}