"Lobster Fever" Sweeps the Globe as Huang Renxun Detonates the Scene with Open-Source Models! Nvidia (NVDA.US) Full-Stack Ambitions Support "AI Bull Market Narrative"

As Anthropic launches Claude Cowork and OpenClaw (the so-called “Lobster”), autonomous AI agents capable of executing tasks worldwide, “AI chip superpower” NVIDIA (NVDA.US) aims to seize this wave of AI proxy development. The company has released its open-source large model “Nemotron 3 Super,” designed to run extremely complex agent-based AI systems in a scalable manner. In Pinchbench benchmarks, Nemotron 3 Super dominates, maintaining the top spot among open-source models. It achieved an 85.6% success rate on OpenClaw tasks, performance comparable to Claude Opus 4.6 and GPT-5.4, two leading closed-source models.

NVIDIA’s latest move significantly strengthens its transition from a pure AI chip supplier to a full-stack platform—covering models, toolchains, cloud inference services, and AI ecosystems—valued at around $4.5 trillion. This shift could soon push its stock price to new highs and drive the global AI computing industry into a new growth cycle. In a statement, NVIDIA said this model combines cutting-edge inference capabilities, enabling high-precision, efficient handling of massive AI tasks suitable for enterprise autonomous AI agents.

NVIDIA states that this new 120 billion parameter open model uses a Mixture of Experts (MoE) architecture, incorporating three innovations. Compared to the previous Nemotron Super, inference performance has increased over threefold, throughput up to five times higher, and accuracy doubled.

NVIDIA reports that AI search leader Perplexity is now offering Nemotron 3 Super to its users for AI-driven systematic search, as one of 20 orchestration models in its system. Tech companies providing advanced software development agents, such as CodeRabbit, Factory, and Greptile, are integrating this model with their proprietary large AI models into their AI agent services, achieving higher accuracy at lower costs and significantly improving enterprise efficiency.

According to NVIDIA, life sciences and cutting-edge AI research institutions like Edison Scientific and Lila Sciences will leverage this flagship open-source model to support their agent-based workflows, including deep literature retrieval, data science, and molecular understanding.

NVIDIA adds that major companies like Amdocs, Palantir (PLTR.US), Cadence (CDNS.US), and European giants Dassault Systèmes and Siemens are actively deploying and customizing Nemotron 3 Super to realize automated workflows in telecommunications, cybersecurity, semiconductor design, and manufacturing, or to develop comprehensive subscription-based products.

In terms of architecture and deployment, Nemotron 3 Super is not just a simple 1200-billion-parameter model. It is designed as a “high total parameters, low activation” system optimized for enterprise agent workflows: 120 billion total parameters, only 12 billion active during inference, with a native context window supporting 1 million tokens, and a minimum deployment threshold of 8×H100 80GB. Its backbone combines LatentMoE, Mamba-2, and a small amount of Attention, with two shared-weight MTP (Multi-Token Prediction) layers. The technical report states the base model has 88 layers, a dimension of 4096, 32 Q heads, 2 KV heads, 512 experts per layer, Top-k activation of 22 experts, and MoE latent size of 1024.

The engineering significance of NVIDIA’s innovative open-source AI model design is clear: using MoE to control activation costs, Mamba to extend context and throughput, and Attention to ensure precise retrieval and stable reasoning. It functions more as an “agent orchestration brain” for multi-agent coordination, long-chain tool invocation, and extended context memory, rather than just a single-turn dialogue model.

At the Barcelona Mobile World Congress (MWC Barcelona), Qualcomm (QCOM.US) CEO Cristiano Amon recently stated that the upcoming “AI Agent” super wave will transform the broader digital ecosystem.

Amon predicts 2026 will be the “Year of AI Agents.” “We will shift from a mobile-centric, app-centric digital ecosystem to an agent-centric, cross-era ecosystem,” he said. “AI agents will become central. They won’t just respond to you—they will observe, interpret, and act.”

NVIDIA’s Ambition: More Than Just a Chip Supplier—Becoming an “AI Infrastructure Contractor”

From an efficiency perspective, Nemotron 3 Super’s key advantage is not just superior accuracy but reducing inference throughput and costs at similar accuracy levels. NVIDIA’s technical report states that under an 8k input / 64k output setting, Nemotron 3 Super achieves 2.2 times the inference throughput of GPT-OSS-120B and 7.5 times that of Qwen3.5-122B; their blog also claims over fivefold throughput improvement over the previous Nemotron Super.

NVIDIA notes that as global enterprises move beyond AI chatbots toward multi-agent applications, they face two main limitations.

First is context explosion: multi-agent workflows can generate tokens at least 15 times higher than standard chat, since each interaction requires resending full history, including tool outputs and intermediate reasoning. Second is the “thinking tax”: complex agents need to perform reasoning at every step, but using large models for each subtask becomes prohibitively costly and slow, making practical deployment difficult.

Nemotron 3 Super’s 1 million token context window allows agent workflows to retain full state in memory and prevent drift. The model’s 120 billion parameters are only 12 billion active during inference.

In multi-agent AI workflows, inference tasks typically involve running a large trained model to produce predictions or inferences on massive, even previously unseen, data.

On NVIDIA’s Blackwell platform, the model runs at NVFP4 precision, reducing memory needs and achieving inference speeds up to four times faster than Hopper FP8, without any loss in accuracy.

The model is trained on synthetic data generated by state-of-the-art inference models. Nemotron 3 Super is fully open-source and available via build.nvidia.com, Perplexity, OpenRouter, and Hugging Face.

Nemotron 3 Super exemplifies NVIDIA’s “full-stack signal”: it’s not just a model for sale but integrated into a comprehensive AI ecosystem—including 120B total parameters, 12B active parameters, 1 million token context, Blackwell optimization, NVIDIA’s NIM microservice ecosystem, NeMo fine-tuning, cloud computing, and local deployment partners—serving highly complex agent workflows. NVIDIA is expanding its core value chain from “selling AI acceleration cards” to “defining agent models, inference stacks, deployment paths, and enterprise workflows,” increasingly acting as an “AI infrastructure contractor” rather than just a chip supplier.

NVIDIA states that Dell Technologies (DELL.US) is integrating Nemotron 3 Super into the Hugging Face platform’s Dell Enterprise Hub for fully localized deployment on Dell AI Factory, advancing enterprise multi-agent AI workflows. HPE (HPE.US) is also incorporating Nemotron into its agents hub to support scalable enterprise deployment.

Since December last year, NVIDIA has been releasing the Nemotron 3 series as open-source models. The company will hold its global AI superconference, GTC 2026, from March 16-19, showcasing breakthroughs transforming industries—from physical AI and AI factories to agent-based AI and inference.

AI GPU + CUDA Fortress Grows Stronger, NVIDIA’s Stock Nears All-Time High?

NVIDIA’s official blog states that Nemotron 3 Super achieved 85.6% on the PinchBench suite, making it the “best open-source model” in its category. On OpenClaw tasks, it scored 85.6%, performance close to Claude Opus 4.6 and GPT-5.4. This positions Nemotron 3 Super as one of the most scalable “agent brains” among open-source and paid closed-source options, suitable for complex multi-step proxy, long process orchestration, and mixed workload environments.

Regarding its moat, under CEO Jensen Huang, NVIDIA’s “super AI moat” built on AI GPU power and CUDA has become even more solid with Nemotron 3 Super’s release. Its role increasingly resembles an AI infrastructure contractor, not just a chip vendor.

According to NVIDIA’s official blog, Nemotron 3 Super is optimized for Blackwell inference efficiency and agent scenarios, not just running on NVIDIA GPUs. It offers up to 5 times higher throughput and 2 times better accuracy than previous models, with inference speeds at 2.2 times GPT-OSS-120B and 7.5 times Qwen3.5-122B in 8k input / 64k output settings, and up to 4 times faster than Hopper FP8 on Blackwell with NVFP4. This “model architecture—quantization format— inference framework— flagship GPU platform” synergy makes CUDA, TensorRT-LLM, NIM, DGX/Blackwell integration more resilient to single-variable disruptions. It also signals that NVIDIA is elevating its moat from “single-GPU performance and CUDA barriers” to a comprehensive AI system—covering model architecture, inference stack, GPU platform, and enterprise deployment.

Wall Street analysts’ sentiment has shifted more bullish with Nemotron 3 Super’s debut. The trend toward a “full-stack AI platform” is likely to push NVIDIA’s stock past its previous all-time high of $212.167, with current close at $186.03.

Morgan Stanley analysts reaffirm NVIDIA as the “top pick” in semiconductors, maintaining an “Overweight” rating and a $260 target price, emphasizing this is an optimal entry point. According to TIPRANKS, the average analyst target is around $273, implying a 47% upside over the next 12 months.

Recent channel surveys show that the global “AI compute supply-demand gap” is widening daily in single digits, with hyperscalers aggressively expanding AI workloads. Even with some hyperscaler clients (like Amazon and Meta) developing their own AI ASICs or purchasing AMD AI GPUs, NVIDIA’s product procurement is expected to grow over 80% by 2026.

The upcoming GTC 2026 will showcase NVIDIA’s leading technology roadmap, addressing concerns about market share loss. The Vera Rubin architecture and NVIDIA’s latest physical AI initiatives will open new TAM opportunities.

As model size, inference chains, and multimodal/agentic AI workloads exponentially increase compute demands, major tech companies are shifting capital expenditure toward AI infrastructure. Investors continue to see NVIDIA and AMD’s new products and AI clusters as the most promising bullish narratives, with investments in power, liquid cooling, optical interconnects, and related supply chains remaining hot despite geopolitical uncertainties.

According to recent analyst forecasts, Amazon, Google’s parent Alphabet, Meta Platforms, Oracle, and Microsoft are expected to spend around $650 billion on AI-related capital expenditures by 2026, with some estimates exceeding $700 billion—an increase of over 70% year-over-year. These five giants are projected to invest about $1.5 trillion in AI infrastructure from 2023 to 2026, compared to roughly $600 billion accumulated before 2022.