NVIDIA

After Nvidia's earnings, the market is reframing its moat. The worry used to be that AWS and Google would eat GPU share with custom chips. The new focus: at gigawatt-scale, orchestration and interconnects are harder than raw compute. Nvidia's Vera Rubin rollout bundles NVLink switches, Spectrum-X Ethernet, and BlueField DPUs to squeeze efficiency at the rack level. The post doesn't give specific performance numbers, but the logic is clear—rivals can match a single chip, but struggle to match Nvidia's full-rack delivery.

Nvidia is reportedly buying Hugging Face for $13B, after a $6B deal for Poolside and Stripe's $7B+ acquisition of OpenRouter. All three targets give away model weights for free. The article argues Nvidia wants to reduce reliance on hyperscalers and frontier labs, but the post doesn't detail deal terms or integration plans.

CFO Colette Kress gave a fiscal 2028 revenue guide of roughly $673B on Aug 26, implying 70% growth—well above the 44% analyst consensus. The just-reported quarter hit $96.2B in revenue and $89B in data-center sales, up 117% YoY. Huang says demand far exceeds 70%, but component shortages (memory, etc.) cap what they can ship. The customer base is broadening beyond hyperscalers to regional AI firms, neoclouds, startups, and enterprises, grouped under the label ACIE. Nvidia is also financing its own demand: $105B in support for an Ohio compute campus and a partnership aiming for up to $500B in data-center financing. The post notes the circular-financing concern but only quotes Huang calling the risk low; no independent risk assessment is provided.

Nvidia has reportedly agreed to buy Hugging Face for $12.9 billion, per The Information. The deal would help Nvidia protect its chip dominance and re-enter cloud services. Business Insider notes no signed agreement yet and talks could still fall apart. Neither company has commented.

NVIDIA confirmed its acquisition of HuggingFace for $13B, roughly 80x the company's $150M ARR. The price nearly doubled NVIDIA's initial $7B offer from January 2026, following HuggingFace doubling its customer base this year. OpenAI also published a retrospective on the HuggingFace incident, though the post doesn't spell out details. Separately, Z.ai released GLM-5.3-Flash, a 320B-parameter open-weight model with 18B active parameters, a 1M-token context window, and an MIT license, running entirely on Chinese chips.

OpenAI shared first benchmarks for its custom inference chip Jalapeño at Hot Chips 37. Against NVIDIA GB200/GB300, Jalapeño delivered 1.5–1.9× more work per watt at peak throughput, 1.7–3.6× lower end-to-end latency, and 2.1–4.1× higher performance on highly interactive workloads. The chip is rated at 700W but reportedly stayed at or below 550W in tested runs. OpenAI plans to deploy it into its own infrastructure by year-end, with Gen 2 deep in development and Gen 3 underway. Separately, GPT-Astra + Codex helped optimize low-level kernels, getting three previously unplanned open-weight models to run 1.5–1.8× faster than human-expert-written code in about two months. SemiAnalysis called it unusually strong for a first-gen ASIC. The post does not disclose pricing, volume, or external customer plans.

Nvidia has been in talks to buy Hugging Face in recent weeks, valuing the open-source model platform at over $13 billion. No deal has been reached and talks could still fall apart. The post doesn't spell out Nvidia's rationale, deal structure, or regulatory risks. Treat this as early-stage contact, not a done deal.

Bloomberg reports that Nvidia held talks to acquire Hugging Face, the open-source model and dataset platform. The post doesn't say whether talks are active, what the offer was, or how Hugging Face responded. A deal would give Nvidia direct control over a key developer hub and model distribution channel. For now, only the fact of discussions is confirmed—hold off on conclusions until both sides comment.

Nvidia guided ~70% YoY revenue growth for FY2028 during its Q2 earnings call. Jensen Huang added that actual demand is far higher—70% is what they can supply, not what the market wants. He noted AI demand is spreading beyond hyperscalers to enterprises and governments, which the market hasn't fully priced in. Q2 revenue hit $96.22B, up 106% YoY, with data center contributing 92%. Q3 guidance is $108B, above the $104B analyst consensus. A risk flag: receivables ballooned from $38.5B to $63B in six months, with payment cycles stretching from 45 to 60 days. Nvidia also announced an additional 2M GPUs for AWS in 2027–2028, spanning Blackwell Ultra, Rubin, and Rubin Ultra.

Amazon will add 2 million Nvidia GPUs—Blackwell Ultra, Rubin, and Rubin Ultra—to AWS data centers in 2027–2028. The deal was announced during Nvidia's earnings call, just five months after Amazon committed to over 1 million GPUs. Nvidia says demand has already exceeded those expectations. No financial terms were disclosed, but the deal is worth tens of billions based on unit costs. The post doesn't spell out how this fits with Amazon's own Trainium and Inferentia chips, or which customers will get the new capacity.

Nvidia just posted over $96 billion in quarterly revenue, putting it on the verge of a $100 billion quarter. The vast majority came from its data center business. The article doesn't break out profit or year-over-year growth, but the $96 billion figure alone dwarfs many tech giants' annual revenue.

Anthropic keeps spending big on compute. It signed a roughly $45 billion, six-year deal to rent AI infrastructure from UK-based Nscale. Nscale will supply compute using Nvidia's new Vera Rubin chip system, starting in late 2027. Nscale was founded in 2024 and already has a deal with Microsoft.

Nvidia reported H1 FY2027 revenue of $177.8B and net profit of $118B, with gross margin at 75%. Q2 revenue hit $96.2B, up 106% YoY, driven by data center revenue of $89B, up 117% YoY. Q3 revenue guidance is $108B ±2%. The Vera Rubin platform is in full production, and Nvidia is mobilizing $500B in third-party capital for AI infrastructure.

The FT reframes Nvidia's business as 'balance sheet-as-a-service': it uses its own equity and customer commitments to help cloud providers and startups finance GPU purchases. The piece estimates Nvidia has mobilized roughly $200bn in capital through guarantees, investments, and vendor financing, though the full breakdown isn't spelled out. The core risk: if AI demand cools, Nvidia's balance sheet gets hit by both a stock drop and customer defaults at the same time.

The FT tallies global AI data centre investment plans at over $7tn and warns demand may not justify the buildout. AI revenue is still concentrated in hardware makers like Nvidia, while downstream apps haven't proven they can generate matching returns. The $7tn figure is a multi-year aspirational total, not annual spend, but the core caution stands: if enterprise customers don't pay up, these facilities and chips become stranded assets.

NVIDIA booked $96B in Q2 and guided $108B for Q3, crossing $100B in a single quarter for the first time. Hyperscaler revenue grew only 13% sequentially, while neoclouds and AI startups drove 25% growth and now account for most net-new Data Center revenue. To fund those buyers, NVIDIA extended payment terms—DSO jumped from 45 to 60 days and receivables hit $63B. If DSO keeps climbing, it signals more supplier financing is needed to sustain demand.

Perplexity released Portable Computer, a local-first version of its Computer agent that runs on the NVIDIA DGX Spark. It uses Qwen 3.8 27B or PPLX 27B to handle files, code, and workflows entirely on-device—no credits burned and private data stays put. When a task needs web search or frontier reasoning, the local orchestrator asks permission before escalating to the cloud. Available now for Pro and Max subscribers on Linux; Windows support is coming soon.

A Nvidia employee has been charged by the US Department of Justice with smuggling export-controlled advanced chips into China. The post only discloses the headline so far—chip models, smuggling methods, and amounts involved are not spelled out. This lands as US-China chip controls keep tightening, putting Nvidia's compliance risk directly in the spotlight.

NVIDIA claims the Vera Rubin NVL72 rack-scale system delivers up to 30x more work per watt than H100 when running AI agent inference. The internal test used Llama 3.3 70B on agent workflows. The post doesn't disclose latency figures or full test configs, so treat 30x as a peak number. The system is slated for second-half 2026 and targets inference and agent workloads.

Tomasz Tunguz maps AI infrastructure shortages as a Bullwhip Effect: the 2023 GPU rush cut server shipments 22% and starved memory fabs; 18 months later HBM conversion drove enterprise SSD prices up 80% in a quarter. By late 2025 agentic workflows pushed CPU-to-GPU ratios toward 1:1, lifting Intel Xeon ASPs 27%. In 2026 nearline HDD production is fully sold out. The steepest cost is the data center shell itself—$20B per gigawatt, with transformer lead times near three years and GE Vernova and Siemens Energy turbine backlogs stretching to 2031. The post warns that new capacity arriving in 2027–2028 could crack the whip if software revenue doesn't keep pace.