"Hot Lobsters" Sweeps the Globe, Huang Renxun Launches Open Source Model to Dominate! NVIDIA Corporation (NVDA.US) Ambitious Full-Stack Supports "AI Bull Market Narrative"

With the popularity of AI agents such as Claude Cowork launched by Anthropic and OpenClaw (also known as "lobster") that can autonomously perform tasks worldwide, "AI chip superpower" NVIDIA Corporation (NVDA.US) is determined to seize this wave of AI agent super-typhoon. The company has launched its open-source large model "Nemotron 3 Super" specifically designed for ultra-large-scale AI agents, aiming to run extremely complex agent-based AI intelligent systems in a scalable manner. In terms of Pinchbench benchmark testing, Nemotron 3 Super sits comfortably at the top of the open-source rankings. In terms of the OpenClaw task success rate, it scored a high 85.6%, with performance approaching the Claude Opus 4.6 and GPT-5.4 closed-source models. This latest move by NVIDIA Corporation significantly strengthens the company's transition from a simple AI chip supplier to a "model-toolchain-cloud inference service-AI ecosystem" full-stack platform, potentially leading to another historical high in the company's stock price and driving the global AI computing industry towards a new upward trajectory. The ambition of "NVIDIA Corporation's full-stack AI" is taking center stage in the capital market narrative amid the geopolitical turmoil caused by the GEO Group Inc in the Middle East, striving to support the "AI bull market" with all its might. In an official statement, NVIDIA Corporation stated that this model combines state-of-the-art inference capabilities, capable of efficiently and accurately completing massive AI tasks, suitable for enterprise-level autonomous AI agent systems. The company stated that this new 120 billion parameter open model uses a mixture of expert model (MoE) architecture, incorporating three innovations. Compared to the previous generation Nemotron Super model, the inference performance has more than tripled, throughput can be increased up to 5 times, and accuracy can be increased up to 2 times. NVIDIA Corporation pointed out that AI search leader Perplexity is beginning to provide Nemotron 3 Super to its users for systematic searches driven by AI agents, and as one of the 20 encoding models in Computer. Technology companies providing advanced software development agents, such as CodeRabbit, Factory, and Greptile, are integrating this model with their proprietary AI large models into their AI agent services to achieve higher accuracy at lower costs and significantly improve enterprise operational efficiency. According to NVIDIA Corporation, life science and cutting-edge AI research institutions such as Edison Scientific and Lila Sciences will use this flagship open-source model to provide significant support for their agent modes, for complex functions such as deep literature retrieval, data science, and molecular understanding. NVIDIA Corporation also noted that telecommunications companies such as Amdocs, leading US AI + data analytics company Palantir(PLTR.US), EDA chip design software leader Cadence(CDNS.US), and European giants Dassault Systemes and Siemens are actively deploying and customizing this model to achieve the vision of agent-style automation in telecommunications, network security, semiconductor design and manufacturing workflows or comprehensive update and subscription-based products. From the perspective of model structure and deployment parameters, Nemotron 3 Super is not simply stacking parameters to 120 billion, but rather being made more suitable for enterprise-level agent-style workflows with a "high total activation, low activation" system: total parameters of 120B, only activating 12B during inference, native support for a context window of 1 million tokens, and a minimum deployment threshold of 8H100 80GB. Its backbone consists of a mixed architecture of LatentMoE+Mamba-2+low attention, and two shared-weight MTP(Multi-Token Prediction) layers. According to the official technical report, this base model has 88 layers, model dimensions of 4096, 32 Q heads, 2 KV heads, 512 experts per layer, Top-k activation of 22 experts, and a MoE Latent Size of 1024. Therefore, the engineering significance of this innovative open-source AI agent model from NVIDIA Corporation is clear: using MoE to control activation costs, using Mamba to extend context and throughput, using Attention to maintain precise retrieval and inference stability. Therefore, it is more like an "agent orchestration brain" aimed at orchestrating multiple agents, tool calls, and long-context memories, rather than simply pursuing high scores in single-round conversations. The CEO of Qualcomm, the world's largest smartphone chip company, Cristiano Amon, recently stated at the Mobile World Congress in Barcelona that the upcoming "AI agent super typhoon will change a wider digital ecosystem. Amon also mentioned that 2026 will be the "year of AI agents". "We will transition from a mobile-centric, app-centric digital ecosystem to an era-centric ecosystem centered around agents," he said. "AI agents will be at the center. They won't just respond to you. They will observe, interpret, and act." NVIDIA Corporation's ambition: not only to be a chip supplier, but also to be an "AI infrastructure general contractor". In terms of efficiency indicators, the absolute highlight of Nemotron 3 Super is not absolute accuracy dominance, but the ability to "reduce the inference throughput and cost of agent systems in complex, multi-step, long-process code/terminal/tool call hybrid workloads with similar accuracy." According to NVIDIA's official technical report, under the setting of 8k input/64k output, Nemotron 3 Super's inference throughput can reach 2.2 times that of GPT-OSS-120B and 7.5 times that of Qwen3.5-122B. The official blog also states that it can achieve more than a 5x increase in throughput compared to the previous generation Nemotron Super, and in a Blackwell environment, it can be as fast as 4 times faster than Hopper FP8. This kind of "model architecture-quantization format-inference framework-flagship GPU platform" synergy makes it difficult for CUDA, TensorRT-LLM, NIM, DGX/Blackwell's linkage to be replaced by any single variable. It also demonstrates that NVIDIA Corporation is transitioning its core value chain from selling AI acceleration cards to defining agent models, inference stacks, deployment paths, and enterprise workflow entry points, making its role more like an AI infrastructure general contractor rather than just a chip supplier. NVIDIA Corporation stated in its model declaration that as global enterprises' operational needs driven by AI applications gradually exceed AI chatbots like Siasun Robot & Automation, and move towards multi-agent applications, they face two limitations. The first limitation is context explosion. The number of tokens generated by multi-agent workflows can be up to at least 15 times that of standard chatbots, as each interaction requires sending the complete history, including tool outputs and intermediate reasoning processes. The second limitation is thinking tax. According to the company's statement, extremely complex agents must reason at each step, but if each sub-task uses large model parameters, multi-agent applications can become prohibitively expensive and very slow to respond, making it difficult to implement in actual business operations. NVIDIA Corporation stated that Nemotron 3 Super has a context window of 1 million tokens, allowing the agent mode workflow to retain the complete workflow state in memory and prevent target drift. The company also added that out of its 120 billion parameters, only 12 billion parameters are active during inference. In multi-agent AI workflows, AI inference tasks typically refer to running a training workflow with a large AI model to make predictions or draw deductive conclusions about the entirely new, or even previously unseen tasks, such as Beijing Vastdata Technology. On NVIDIA Corporation's Blackwell platform, this model operates with NVFP4 precision. This also means that NVIDIA Corporation has lowered memory requirements to a lower level and has increased inference speed to up to 4 times that of the company's Hopper platform FP8 without sacrificing any degree of accuracy. The company pointed out that this model is based on deep training with synthetic data generated by cutting-edge inference models. Nemotron 3 Super can be obtained completely open-source through build.nvidia.com, Perplexity, OpenRouter, and Hugging Face. Nemotron 3 Super itself is a very typical "all-stack signal" released by NVIDIA Corporation: it is not just about selling models alone, but rather integrating the total 120 billion parameters/12 billion activation parameters, 1 million level token contexts, Blackwell optimization, NVIDIA Corporation NIM service ecosystem, NeMo fine-tuning ecosystem, and NVIDIA Corporation cloud computing system into a complete NVIDIA Corporation-led software and hardware integrated AI ecosystem product system that directly serves extremely complex agent-style workflows. NVIDIA Corporation is expanding its core value chain from "selling AI computing acceleration cards" to "defining agent models, inference stacks, deployment paths, and enterprise workflow entrances," making it more of an AI infrastructure general contractor rather than just a chip supplier. NVIDIA Corporation stated that AI server manufacturing giant Dell Technologies, Inc. Class C Technology (DELL.US) is incorporating this model into the Dell Enterprise Hub on the Hugging Face platform for fully localized deployment on the Dell AI Factory platform to advance enterprise multi-agent AI workflows. NVIDIA Corporation also stated that another AI server leader, Hewlett Packard Enterprise Co. Technology (HPE.US), is also adding Nemotron to its agents hub to help ensure the scalable implementation of agent-based AI in enterprises. NVIDIA Corporation began releasing the Nemotron 3 series of open-source models in December last year. In addition, this US tech giant is set to host its global AI mega conference, the NVIDIA Corporation GPU Technology Conference (GTC), from March 16 to 19 next week. The company stated that from physical AI, AI factories to agent-based AI and inference, GTC 2026 will showcase groundbreaking technological advancements reshaping various industries. With the AI GPU + CUDA moat becoming increasingly solid, will NVIDIA Corporation's stock price set a new historical high? NVIDIA Corporation stated in its official blog post that Nemotron 3 Super achieved 85.6% in the full PinchBench test set, making it the best open-source model of its kind according to their standards. It also scored a high 85.6% in the OpenClaw task success rate, approaching the performance level of Claude Opus 4.6 and GPT-5.4. Therefore, a more accurate positioning for NVIDIA Corporation's newly introduced Nemotron 3 Super is: if a typical business needs to do complex multi-step agents, long-flow orchestrations, and mixed workloads of code/terminal/tool calls, Nemotron 3 Super may not be the strongest single point, but it is likely one of the closest to a "scalable deployed agent intelligence brain" within the current open-source + paid closed-source camps. Regarding the moat, NVIDIA Corporation, led by founder Jensen Huang, has undoubtedly strengthened its "super AI moat" built on the AI GPU power system + CUDA with the high-profile launch of Nemotron 3 Super. As mentioned earlier, its role is increasingly resembling an AI infrastructure general contractor, rather than just an AI chip supplier. According to NVIDIA Corporation's official blog post, Nemotron 3 Super is not only running efficiently on the NVIDIA Corporation GPU platform, but it has been explicitly designed to be a large model for Blackwell inference efficiency and agent scene optimization. The official stated that compared to the previous generation Nemotron Super, it achieves a 5x increase in throughput and a 2x increase in accuracy at the highest levels. In the 8k input/64k output setting, the throughput can reach 2.2 times that of GPT-OSS-120B and 7.5 times that of Qwen3.5-122B. And when running massive inference tasks on Blackwell with NVFP4, it can be as fast as 4 times faster than Hopper FP8. This synergy of "model architecture-quantification format-inference framework-flagship GPU platform" will make it difficult for CUDA, TensorRT-LLM, NIM, and DGX/Blackwell's linkage to be replaced by any single variable. It also indicates that NVIDIA Corporation is transferring its moat from "single GPU performance and CUDA barrier" to a complete set of AI system capabilities such as "model architecture-inference stack-GPU platform-enterprise deployment". Recent attitudes of Wall Street analysts towards NVIDIA Corporation have become more positive on the margins with the high-profile launch of Nemotron 3 Super. The emergence of Nemotron 3 Super and the shift of NVIDIA Corporation towards a "full-stack AI platform" trend will undoubtedly jointly push NVIDIA Corporation's stock price to surpass its previous historical peak set in October at 212.167 USD. As of the closing of the US stock market on Wednesday, NVIDIA Corporation's stock price closed at 186.03 USD. The latest channel survey from financial giant Morgan Stanley shows that the global "AI computing supply-demand gap is expanding significantly on a daily basis", and cloud computing giants (Hyperscalers) are still extremely aggressive in their growth of AI workloads. According to Morgan Stanley's research, even if some Hyperscalers clients (like Amazon.com, Inc. and Meta) use their own AI ASIC or purchase AMD AI GPU computing clusters, it is expected that these super clients' purchases of NVIDIA Corporation products will still increase by over 80% in 2026. Morgan Stanley pointed out that the upcoming GTC 2026 conference will showcase the company's leading technological roadmap, effectively countering market concerns about market share loss; the Vera Rubin architecture and NVIDIA Corporations latest layout in physical AI will open up brand-new market value (TAM) spaces. When model scale, inference chains, and multimodal/agent-style Agentic AI workloads drive computing consumption to exponentially expand outward, the main capital expenditure focus of tech giants is increasingly centralized on AI computing infrastructure. Global investors are anchoring the "AI bull market narrative" around NVIDIA Corporation, AMD's new product iterations, and the delivery of AI computing clusters as one of the most certain prosperous investment narratives in the global stock market, indicating that investments related to AI training/inference like electricity, liquid cooling systems, optical interconnections, and other supply chain investments closely related to AI will continue to rank among the hottest investment camps in the stock market even amidst the geopolitical uncertainties faced by the GEO Group Inc in the Middle East. According to the latest analyst expectations compiled by institutions, Amazon.com, Inc. along with its parent company Alphabet Inc. Class C Alphabet, Meta Platforms Inc., Oracle Corporation, and Microsoft Corporation are collectively expected to reach a cumulative AI-related capital expenditure of approximately $650 billion in 2026. Some analysts believe that the overall expenditure may exceed $700 billion, indicating that the year-on-year increase in AI capital expenditure could exceed 70%. It is worth noting that these top five US tech giants are expected to invest around $1.5 trillion from 2023 to 2026 to build an extremely large AI computing infrastructure; in comparison, these tech giants collectively invested approximately $600 billion during the entire historical period before 2022. In summary, NVIDIA Corporation's Nemotron 3 Super launch is a major milestone that is expected to reshape the AI computing industry. It represents the company's continued push towards becoming a comprehensive provider of AI solutions and services, beyond just supplying hardware. The advancements in AI technology and the increasing adoption of AI across various industries are expected to drive NVIDIA Corporation's stock price to reach new heights and position the company at the forefront of the AI revolution.