Price
Ownership breakdown — NVDA
Percentage of outstanding shares freely tradable on the open market — not locked by insiders, strategic holders, or transfer restrictions.
NVDA → At 95.9% public float, nearly all NVDA shares can change hands on the open market without restriction. That depth of free float is what makes NVDA one of the most liquid large-cap names in the world — tight spreads, easy entry/exit for funds, and reliable inclusion in passive index flows. Recent daily volume is about 134.95M shares.
Aggregate share held by registered investment managers (Vanguard, BlackRock, pension funds, etc.), summed from SEC Form 13F quarterly filings.
NVDA → Not available on the current FMP plan — the /institutional-ownership/extract endpoint requires a paid tier. EDGAR 13F aggregation runs daily as fallback.
Shares held by executives, directors, and other affiliates — typically subject to trading windows and disclosure via SEC Form 4.
NVDA → Cannot be derived until institutional ownership is populated — computed as 100% − public float − institutional.
Prices and market cap refresh from Twelve Data + FMP /quote on the hero ticker poll. Float % and FMP shares come from FMP /shares-float. EDGAR companyfacts cross-checks shares outstanding and public float (USD); EDGAR 13F bulk data fills institutional % when FMP 13F extract is plan-blocked. Sources: fmp-shares-float, edgar-companyfacts.
Supply chain
Weekly Dispatch
Groq 3 LPX joins Vera Rubin NVL72 in production; NVIDIA posts agentic efficiency metrics; B200/B300 still OEM baseline
Late August NVIDIA announced Groq 3 LPX — a low-latency inference accelerator codesigned with Vera Rubin NVL72 — is in full production, with Nebius as first cloud adopter. NVIDIA's Aug 27 blog publishes measured agentic efficiency figures (up to 30× throughput/MW vs GB300 NVL72 on SemiAnalysis AgentX — pending SemiAnalysis review). Vera CPU is described as shipping. HGX B200/B300 remain the general OEM HGX baseline.
Competitors
Signal, aggregated
How a chip becomes a product
NVIDIA doesn't build a GPU alone — it's the product of a chain that runs through a dozen industries before it ever reaches a datacenter or a desk. Here's that chain, start to finish.
Before a single transistor is drawn, engineers need software just to design the chip. EDA (Electronic Design Automation) tools let designers lay out billions of transistors and simulate how they'll behave — catching errors here costs a bug fix; catching them after fabrication costs a $100M wafer run.
This is where NVIDIA itself sits. A "fabless" designer owns no factories — its product is the chip's architecture, not the physical silicon. NVIDIA designs the GPU; someone else has to actually build it.
Manufacturing starts with raw materials: ultra-pure silicon wafers, plus a long list of rare-earth elements and specialty chemicals that never make headlines but without which nothing else on this list works.
Turning silicon and chemicals into a working chip takes some of the most complex machines ever built — lithography systems that print circuits smaller than a virus, and the etching and deposition tools that build a chip up layer by layer.
The foundry is where the chip is actually fabricated — hundreds of process steps over weeks, on wafers that cost more than a car. NVIDIA doesn't own a foundry; it pays someone else to run theirs.
A smaller group of companies straddle both worlds, fabricating logic chips and memory side by side under one roof.
A GPU without memory next to it is just an expensive calculator. High-bandwidth memory (HBM) sits inches from the chip, feeding it data fast enough to keep up with it.
A freshly fabricated wafer isn't a product yet — it has to be cut into individual dies, tested, and packaged into the chip you'd recognize. This step (OSAT — outsourced assembly and test) is where defective dies get caught before they ship anywhere.
Individual chips get assembled onto boards and into full servers here — the unglamorous, extremely high-volume work of turning components into machines.
The finished servers get a brand on them and go out the door under a name enterprise buyers recognize and order directly from.
For GPUs sold to consumers rather than datacenters, board partners build the actual graphics card around NVIDIA's chip — the cooler, the PCB, the box you'd see on a shelf.
A datacenter full of GPUs is only as fast as the network connecting them. High-speed switches and network cards move data between thousands of GPUs working on the same problem simultaneously.
None of this runs without power and cooling. As GPU racks get denser and hotter, keeping them running has become an industry of its own.
Most people never buy a GPU directly — they rent time on one. Cloud providers buy GPUs at enormous scale and resell compute by the hour.
And for the GPUs that do end up in someone's own PC, retail is the last stop between the factory and the desk.
GPU Catalog
NVIDIA datacenter GPUs are fabbed via Samsung Electronics, GlobalFoundries and Taiwan Semiconductor Mfg (ADR) — see the supply-chain graph for foundry → NVDA edges.
| CMP | SKU | VENDOR | ARCH | MEM | BW | TDP | FP16 | DOCS |
|---|---|---|---|---|---|---|---|---|
AMD Radeon RX 7900 XTX RX-7900-XTX Consumer GPU reference; not datacenter AI but competes with GeForce. | amd | RDNA3 | 24 GB GDDR6 | 960 GB/s | 355 W | — | [PAGE] | |
AMD Instinct MI300X MI300X-OAM-192GB CDNA3 accelerator; 192GB HBM3; primary NVDA H100 competitor. | amd | CDNA3 | 192 GB HBM3 | 5,300 GB/s | 750 W | — | [PAGE] | |
AMD Instinct MI325X MI325X-OAM-256GB HBM3e refresh of MI300X; 256GB capacity. | amd | CDNA3 | 256 GB HBM3e | 6,000 GB/s | 1000 W | — | [PAGE] | |
AMD Instinct MI355X MI355X-OAM-288GB CDNA4 generation; targets Blackwell B200 class workloads. | amd | CDNA4 | 288 GB HBM3e | 8,000 GB/s | 1400 W | — | [PAGE] | |
Huawei Ascend 910B ASCEND-910B Primary China-market AI training chip; specs from Huawei Ascend product documentation. | huawei | Ascend | 64 GB HBM2e | — | 400 W | 320 TFLOPS | [PAGE][ARCH] | |
Huawei Ascend 910C ASCEND-910C Successor Ascend generation; public spec sheet limited outside China — verify before production use. | huawei | Ascend | — | — | — | — | [PAGE][ARCH] | |
NVIDIA GeForce RTX 5090 RTX-5090 Consumer Blackwell flagship; 32GB GDDR7. | nvidia | Blackwell | 32 GB GDDR7 | — | 575 W | — | [PAGE][ARCH] | |
NVIDIA B200 SXM B200-SXM-192GB Blackwell datacenter GPU; FP4 native; 5th-gen NVLink. | nvidia | Blackwell | 192 GB HBM3e | 8,000 GB/s | 1000 W | — | [PAGE][ARCH] | |
NVIDIA GB200 Grace Blackwell Superchip GB200-SUPERCHIP 2x B200 GPU + Grace CPU; aggregate 384GB HBM3e per superchip module. | nvidia | Blackwell | 384 GB HBM3e | — | 2700 W | — | [PAGE] | |
NVIDIA H100 PCIe H100-PCIe-80GB PCIe FHFL dual-slot; 350W default TGP mode. | nvidia | Hopper | 80 GB HBM2e | 2,000 GB/s | 350 W | — | [PAGE][PDF] | |
NVIDIA H100 SXM5 H100-SXM5-80GB SXM5 variant; FP8/FP16 from NVIDIA product spec table (sparse peak). | nvidia | Hopper | 80 GB HBM3 | 3,350 GB/s | 700 W | 1,979 TFLOPS | [PAGE][PDF] | |
NVIDIA H200 SXM H200-SXM-141GB Hopper refresh with HBM3e; ~1.4x memory capacity vs H100. | nvidia | Hopper | 141 GB HBM3e | 4,800 GB/s | 700 W | — | [PAGE] | |
NVIDIA L40S L40S-48GB Inference/visualization GPU; Ada architecture, not Hopper/Blackwell. | nvidia | Ada Lovelace | 48 GB GDDR6 | 864 GB/s | 350 W | — | [PAGE] | |
NVIDIA RTX PRO 6000 Blackwell RTX-PRO-6000-BLACKWELL Workstation Blackwell; 96GB GDDR7 ECC. | nvidia | Blackwell | 96 GB GDDR7 | 1,792 GB/s | 600 W | — | [PAGE] | |
Samsung HBM3E 12-Hi Stack SAMSUNG-HBM3E-12H Memory component used by NVDA/AMD accelerators — supply-chain competition, not a compute GPU. | samsung | HBM3E | 36 GB HBM3E | — | — | — | [PAGE] | |
Samsung Mach-1 AI Accelerator MACH-1-AI Inference-focused logic chip roadmap; Samsung also competes as HBM supplier (see supply chain). Not a direct GeForce/datacenter GPU SKU. | samsung | Mach-1 | — | — | — | — | [PAGE][ARCH] |