As the Q2 2026 earnings season draws to a close, global AI computing leader NVIDIA is set to release its latest quarterly results next week (August 13) after the US market close, drawing intense attention from Wall Street. As of the August 6 close, NVIDIA's stock price stood at $187.32, once again hitting a new all-time high, with a year-to-date gain exceeding 145% and a market cap approaching $4.7 trillion. Investors widely expect that, against a backdrop of surging demand for AI large model training and inference, NVIDIA's quarterly revenue could surpass the $45 billion mark for the first time, representing a year-over-year increase of over 120%.
AI Computing Demand Spreads from Cloud to Edge, NVIDIA Supply Chain Under Severe Strain
This explosion in AI computing demand is not limited to centralized procurement by major cloud service providers but shows an accelerating trend of spreading from the cloud to the edge. According to industry chain research data, while the delivery cycle for NVIDIA's flagship H100 GPU has shortened from 52 weeks last year to 26 weeks, the order backlog for the next-generation Blackwell architecture B200 GPU has surged by 40% in the past quarter, with some delivery dates pushed back to Q1 2027.
In July, the three major cloud giants—Microsoft, Google, and Amazon—successively announced additional investments in AI infrastructure, with their combined quarterly capital expenditures exceeding $80 billion. Among them, Microsoft explicitly stated it will triple the scale of its AI computing clusters by 2027, with NVIDIA as its primary GPU supplier. Furthermore, Meta and OpenAI recently signed long-term GPU procurement agreements each worth over $10 billion, further intensifying the supply-demand imbalance for high-end computing chips.
HBM Memory Becomes Computing Bottleneck, SK Hynix Capacity Strained
Notably, the production bottleneck for AI chips is shifting from wafer fabrication to the memory segment. The HBM3E high-bandwidth memory, a core companion to NVIDIA GPUs, is currently supplied almost exclusively by SK Hynix. However, in a profit warning issued on August 5, SK Hynix indicated that its HBM3E capacity utilization is near its limit, making it difficult to meet the explosive growth in NVIDIA's orders in the short term. Consequently, the yield ramp-up for NVIDIA's B200 GPU is slower than expected, raising market concerns that this could be the sole negative factor in this quarter's earnings report.
To alleviate supply pressure, SK Hynix has announced plans to build a second HBM packaging plant in Indiana, USA, expected to begin production in 2027. Meanwhile, Samsung Electronics is also accelerating the mass production of HBM3E, but industry analysis suggests it will take at least 2-3 more quarters for Samsung to receive formal certification from NVIDIA. This means NVIDIA's capacity expansion will remain constrained by HBM supply in the short term.
Wall Street Expectations Diverge: Revenue Beat is Consensus, but Gross Margin Inflection Point Sparks Debate
Currently, the Wall Street consensus estimate for NVIDIA's Q2 revenue is $44.2 billion, but several investment banks have recently raised their forecasts. Morgan Stanley lifted its estimate to $46.8 billion, citing early shipments of the B200 GPU contributing additional revenue. Goldman Sachs is even more optimistic, believing that driven by urgent additional orders from cloud providers, NVIDIA's actual revenue could exceed $48 billion.
However, market opinions diverge on the trajectory of gross margins. The B200 GPU uses a more complex CoWoS-L packaging process with initially low yields, which could put pressure on gross margins. NVIDIA's gross margin was 78.9% last quarter, and some analysts expect it to fall back to around 77% this quarter. However, long-term bulls argue that as TSMC's CoWoS capacity expands from 45,000 wafers per month to 60,000, economies of scale will gradually emerge in the second half of the year, potentially pushing gross margins back above 80% in Q3.
Next-Gen Rubin GPU Architecture Details Leaked, Computing Density to Double Again
Ahead of the earnings release, some technical details of NVIDIA's next-generation Rubin GPU architecture have unexpectedly surfaced. According to semiconductor industry media reports, the Rubin architecture will feature a 12-layer HBM4 stacked design for the first time, increasing single-GPU memory capacity to 288GB with bandwidth exceeding 8TB/s. Additionally, Rubin GPUs will come standard with liquid cooling solutions, and the Thermal Design Power (TDP) is expected to exceed 1500W, nearly a 50% increase over the current Hopper architecture.
This technology roadmap further strengthens market confidence in NVIDIA's long-term competitiveness. Analysts point out that the launch of the Rubin architecture will widen the gap between NVIDIA and competitors like AMD and Intel, solidifying its absolute leadership in AI training and inference. Rubin GPUs are expected to enter mass production in the second half of 2027, which will further drive down AI computing costs and accelerate the penetration of AI applications across various industries.
Philadelphia Semiconductor Index Hits Record High, AI Computing Sector Strengthens Overall
Driven by NVIDIA's stock price, the Philadelphia Semiconductor Index (SOX) briefly broke through the 6,200-point mark during trading on August 6, hitting a historic high. AI computing-related stocks like AMD, Broadcom, and TSMC also rose in tandem. AMD's stock rose 2.3%, boosted by news of full order books for its MI400 series chips; TSMC ADR gained 1.8% on market expectations that its CoWoS capacity expansion will directly benefit from NVIDIA's order growth.
In the ETF market, the Global X Artificial Intelligence & Technology ETF (AIQ) and the VanEck Semiconductor ETF (SMH), both holding NVIDIA as their top position, recorded gains of over 1.5%. Fund flow data shows that US semiconductor ETFs attracted over $3.5 billion in net inflows over the past week, indicating that investor bullish sentiment on the AI computing sector continues to heat up.
Investment Strategy Advice: Focus on Earnings Beat and Supply Chain Risks
For US AI computing investors, NVIDIA's earnings report this week will be a key variable determining the market's short-term direction. If both revenue and guidance beat expectations, it could propel the entire semiconductor sector to new highs. However, investors should also be wary of potential capacity reduction risks stemming from HBM supply shortages and the uncertainty of US chip export control policies toward China.
From a long-term allocation perspective, investors are advised to focus on three main themes: first, GPU leaders like NVIDIA and AMD, which directly benefit from the explosion in AI computing demand; second, core supply chain companies like TSMC and SK Hynix, whose capacity expansion will bring deterministic growth; third, AI computing ETFs like SMH and SOXX, which can diversify individual stock risks and share in the sector's overall dividends.
Overall, NVIDIA's Q2 earnings will be a litmus test for the health of the AI computing industry. Regardless of short-term stock price fluctuations, the long-term investment value of AI computing as the infrastructure for a new technological revolution cannot be ignored.
