NVIDIA

Overview

A GPU manufacturer and the de facto standard for AI compute infrastructure. The infrastructure core of the AI era. Also active in AI research — particularly meaningful in-house work in the directions of embodied AI and agentic AI.

Key People

• Jensen Huang — CEO
• Jim Fan — Senior Research Scientist, AI Agents

Models

• Cosmos 3 Super — Cosmos 3 Super (64B, MIT open weights). Released 2026-06-01. The world's first open omnimodal physical AI foundation model. R-Bench #1 open model.
• Cosmos 3 Nano — small, real-time inference
• Cosmos 3 Edge (coming soon) — edge on-device
• Nemotron 3.5 Lightning — Nemotron 3.5 Lightning (30B MoE / 3B active, OpenMDW-1.1 open weights). Released 2026-08-11. 1M context, distilled from Nemotron 3 Ultra, built for agent workloads.
• Cosmos-H-Dreams — Cosmos-H-Dreams (Apache 2.0). Released 2026-07-22. Action-conditioned world model for surgical robotics simulation; generates photorealistic surgical scenes from robot commands; 600 policy rollouts/40 min vs. 2 days on physical hardware.

Research Streams

• Cosmos 3 (2026-06-01) — physical AI omni-model (unified world generation + reasoning + action). Trained on 20T tokens. MoT architecture. → Cosmos 3 Super
• NeMo Switchyard (2026-08-11) — open-source model routing library for agents; tuning-free routers (LLM classifier, stage router, escalation router) plus tunable ones; internal claim of frontier accuracy at ~1/3 the task cost of Opus 4.8 alone → Model Routing
• Cosmos-H-Dreams (2026-07-22) — action-conditioned world model for surgical robotics; open-source (Apache 2.0); part of NVIDIA Medical Physics Simulation framework → Cosmos-H-Dreams
• Molt (2026-07-27, HF Daily #1, 605 upvotes) — PyTorch-native agentic RL framework from NVIDIA NeMo. vLLM rollout + FSDP2 + Ray async queue. Matches Megatron-based systems without the complexity. → Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning
• Project GR00T — humanoid robot foundation model
• CaP-X (2026-04, open-source) — vibe agents alive in physical world (Jim Fan team)
• DrEureka — LLM writes robot skill training code
• Voyager — lifelong learning agent in the Minecraft environment

Business Significance

• Every frontier lab depends on its compute — Anthropic, OpenAI, DeepMind, Meta, and others
• The OpenAI Broadcom partnership (2026~) is an attempt to diversify away from NVIDIA dependence
• Aggressively recruits talent under the "warm GPUs" motto

Recent Activity

• 2026-08-19: The first meaningful H200 volumes reach China — and the party holding them back is Beijing — ByteDance and Tencent have each taken delivery of about 10,000 H200 processors in recent weeks, the first shipments to reach the mainland. The US cleared each company to purchase up to 100,000; Beijing wants the hardware kept outside the mainland to protect domestic chipmakers, and every purchase needs case-by-case NDRC approval. Export authorisation dates to December 2025, granted in exchange for a 25% cut of every sale to the US government. NVIDIA is reported to be holding around 500,000 H200s built largely for Chinese customers. Why it matters: the constraint on NVIDIA's largest blocked market has switched sides — this wiki has recorded China access as a US export-control question, and the binding limit is now the buyer's own government. The 500,000-unit inventory is the size of the position that turns on it. Trivium attributes the relaxation to the run-up to Xi's US trip; nothing else read states that causal link and it is recorded as Trivium's. One single-source claim is not corroborated: Tom's Hardware states most licensed chips must remain in Hong Kong, which it says cannot power them. → AI Governance (source) (Tom's Hardware) (Benzinga)

• 2026-08-18: AI demand has taken consumer memory with it — DDR5 up as much as 485% in a year — a 128GB DDR5-6400 kit now sells for $3,399, roughly 10× its lowest tracked price of about $329; mainstream 64GB kits exceed $1,000; DDR5-6000 2×32GB kits went from ≈$222 in August 2025 to ≈$1,272, a 473% increase. The stated cause is AI infrastructure absorbing memory production, with hyperscale AI customers reported to be reserving much of the industry's future DRAM capacity, and the shortage spreading to DDR4, SSDs and hard drives. Forecasts read, each attributed and none endorsed: TrendForce expects contract prices to rise through 2026; Gartner sees no relief before late 2027; Counterpoint puts the inflection at Q4 2027; Intel CEO Lip-Bu Tan says the industry told him 2028. Why it matters: it prices the other side of Open-Weights Policy Fight. FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution (arXiv:2608.16157) published the same week that a 753B open-weight model runs on a single workstation GPU by spending host memory and bandwidth — the resource that just went up fivefold. Every figure here is consumer DDR5/DDR4; nothing read gives an HBM or GPU-VRAM price, so the effect on datacenter cost is not established by this source (source) (Tom's Hardware)

• 2026-08-17: NVIDIA takes four positions in one data-centre deal — NVIDIA announced it is guaranteeing SB Energy's PORTS-Pike Technology Campus in Pike County, Ohio, to exclusively host NVIDIA AI compute, alongside OpenAI's agreement for approximately 8 GW-IT there. NVIDIA provides up to $105 billion in financing for the campus and invests $1.5 billion in SB Energy, joining SoftBank Group and OpenAI as investors in the landlord. The credit supports an initial 4.25 GW with an option for a further 3.75 GW, phased from 2028; SB Energy and SoftBank build at least 10 GW of new generation and at least $4.2 billion of regional grid infrastructure. Why it matters: in a single transaction NVIDIA is the chip vendor, the financing guarantor, an equity investor in the lessor, and the exclusivity condition on the site. This wiki has recorded the pattern once before at a tenth the size — Google backstopping the lease payments on the TPUs it sold into Anthropic's $35B SPV — and recorded it then as a compute vendor financing demand for its own product. At $105B the same structure is no longer an exception. Nothing read states what the exclusivity binds: the campus, the lease, or the financing. (source) (NVIDIA) (SEC 8-K) (CNBC)

• 2026-08-11: NVIDIA ships a cheap agent model and, the same day, the software that decides when to use one — NVIDIA released Nemotron 3.5 Lightning, an open-weight 30B hybrid MoE with 3B active parameters and a 1M-token context window, under the OpenMDW-1.1 licence with BF16 and NVFP4 weights on Hugging Face and NGC. It is distilled from the frontier Nemotron 3 Ultra, built with the Nemotron Coalition, and carries interleaved Mamba-2 and MoE layers, multi-token prediction and DFlash speculative decoding. NVIDIA's own figures: 86.5% agent productivity on PinchBench, up to 4x output speed and up to 30% faster agentic task completion against its class; Artificial Analysis independently scores it 24 on its Intelligence Index. Alongside it came NeMo Switchyard, an open-source model routing library that picks a model per step of an agent workflow, shipping tuning-free routers (LLM classifier, stage router, escalation router) plus tunable ones, with an internal claim of frontier-level accuracy at nearly one-third the task-completion cost of Opus 4.8 alone. Why it matters: the pairing is the announcement. A router whose headline is a cost ratio against a competitor's flagship is an argument for using small models inside someone else's workflow, and NVIDIA — which sells the compute either way — is the party with the least reason to care which model wins. Three of the four Lightning figures measure speed or cost rather than capability, and PinchBench appears in no sources/evals/ snapshot this repo holds, so 86.5% has nothing local to check it against. → Nemotron 3.5 Lightning (new), Model Routing (new) (source) (NVIDIA) (NVIDIA Technical Blog) (VentureBeat)

• 2026-07-27: Open Secure AI Alliance launched — NVIDIA convenes ~40 companies behind open weights as a security position — NVIDIA announced the Open Secure AI Alliance, an industry body to build and share open models and tools for AI defenders, operating under the Linux Foundation umbrella and building on the Foundation's Akrites vulnerability-disclosure effort and existing OpenSSF work. First technical contribution: NOOA (NVIDIA-labs OO Agents), an Apache 2.0 research framework for testing, tracing, auditing and governing agent behavior, on GitHub at launch. Stated scope covers the full agent stack — identity, permissions, isolation, guardrails, logs, model formats, multi-model scanning, secure coding workflows. Founding partners include Microsoft, IBM, Red Hat, Hugging Face, Mistral, Cloudflare, CrowdStrike, Palantir, Databricks, GitHub, LangChain, Perplexity, Nous Research, Thinking Machines Lab, SpacexAI and vLLM; OpenAI, Anthropic, Google, Meta and Amazon are all absent. Jensen Huang's stated case: "Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community", and "During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion." Why it matters: NVIDIA has moved from selling compute to both sides of the open/closed divide to publicly taking one side of it, and grounded the argument in a documented incident rather than principle — Hugging Face's own forensic timeline records commercial models' guardrails blocking analysis of attack artifacts. → Open-Weights Policy Fight (source) (NVIDIA) (Linux Foundation)

• 2026-07-27: Molt — PyTorch-native agentic RL framework released (HF Daily #1, 605 upvotes) — NVIDIA NeMo published Molt (arXiv 2607.21653), a compact agentic reinforcement learning training framework. Architecture: vLLM rollout engines + single FSDP2 policy actor on NeMo AutoModel + Ray async queue for decoupled coordination. Key design principle: training only on tokens the policy itself generated (not reference or offline data), avoiding distribution shift artifacts that affect frameworks trained on mixed data. Performance: matches Megatron-based systems on standard agentic RL benchmarks while requiring significantly less infrastructure expertise. Available at github.com/NVIDIA-NeMo/labs-molt. Why it matters: NVIDIA NeMo directly entering the agentic RL infra space challenges Ring-Zero (Ant Group, Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning) and SEED (Tsinghua, SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning) for the emerging position of "standard agentic RL training framework." PyTorch-native lowers the barrier for research groups without Megatron expertise. 605 HF upvotes = top-tier practitioner resonance. → Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning (source) (arXiv) (HuggingFace)

• 2026-07-22: Cosmos-H-Dreams released — open-source world model for surgical robotics simulation — NVIDIA released Cosmos-H-Dreams, an action-conditioned world foundation model that generates photorealistic surgical scene video from robot commands, enabling sim-to-real transfer for surgical robot policy training without physical hardware. Released under Apache 2.0 at HuggingFace (nvidia/Cosmos-H-Surgical-Simulator). Part of NVIDIA's Medical Physics Simulation open-source framework, pairing Cosmos-H-Dreams with classical physics simulators (NVIDIA Warp/Newton engines). Performance: 600 policy rollouts in 40 minutes on a single RTX PRO 6000 GPU (vs. 2 days on a physical benchtop). Simulation fidelity: deformation, blood, smoke, fluoroscopy imagery. Partners at launch: CMR Surgical, Johnson & Johnson MedTech, Medtronic — all building on the platform. Why it matters: surgical robot training is one of the hardest embodied-AI domains because real surgical data is expensive, dangerous to collect, and ethically constrained. Cosmos-H-Dreams makes sim-to-real transfer tractable for this domain and opens it to academic and startup groups without access to expensive OR hardware. → Cosmos-H-Dreams (source) (HuggingFace blog) (NVIDIA blog)

• 2026-06-17: ENPIRE — agentic robot self-improvement on real hardware — Jim Fan's NVIDIA GEAR Lab (with CMU, UC Berkeley) published ENPIRE: a system where a fleet of 8 real robots autonomously runs its own research loop — reading papers, proposing hypotheses, resetting physical scenes, running trials, verifying results, and rewriting control code — with zero human researchers in the loop. Results: 99% pass@8 on contact-rich tasks (GPU seating, zip-tie tying). New finding: a physical scaling law — 8 parallel robots improve policies superlinearly faster than fewer robots. The first empirical demonstration of a data-parallel scaling law for real-world physical manipulation. → ENPIRE: Agentic Robot Policy Self-Improvement in the Real World, Jim Fan (source) (arXiv)

• 2026-06-07: EgoScale — humanoid dexterous manipulation from egocentric human video — Jim Fan's team trained a humanoid with 22-DoF dexterous hands (Sharpa Wave tactile, on a Unitree H2 Plus chassis) to assemble model cars, operate syringes, sort poker cards, and fold shirts. Training data: 20,000+ hours of egocentric human video, zero robot teleoperation. Key finding: log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, which directly predicts real-robot success rate. Also demonstrated live VR teleoperation inside a virtual environment on Unitree G1 (PICO headset). Open-sourced: weights, code, post-training dataset, eval set, whitepaper. → Jim Fan (source)

• 2026-06-01: NVIDIA Cosmos 3 released — the world's first fully open omnimodal physical AI foundation model. Cosmos 3 Super (64B, MIT license), Nano, Edge (coming soon). R-Bench #1 open model. Trained on 20T tokens (1B images, 400M videos). Cosmos Coalition launched: global partnerships including Agile Robots, Black Forest Labs, Runway, Skild AI. → Cosmos 3 Super (source)

• 2026-04: CaP-X open-sourced (Jim Fan) (source)

Strategic Position

• Compute supplier + AI researcher dual role
• Particular emphasis on embodied AI — Project GR00T is the cornerstone
• Compared to AI labs, emphasizes "infrastructure for agents to operate in environments" over the models themselves

Related

• Jim Fan
• Embodied Agents
• Cosmos 3 Super
• Cosmos-H-Dreams — surgical robotics world model (Jul 22, Apache 2.0)
• Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning — agentic RL training framework (Jul 27, HF Daily #1)
• Open-Weights Policy Fight — convener of the Open Secure AI Alliance (Jul 27)
• Nemotron 3.5 Lightning — 30B MoE open-weight agent model (Aug 11)
• Model Routing — NeMo Switchyard, shipped the same day (Aug 11)
• Project GR00T