NVIDIA Corporation (NASDAQ:NVDA) is one of the Top AI and Technology Stocks to Buy According to Hedge Funds. On December 12, Reuters reported that NVIDIA Corporation (NASDAQ:NVDA) informed Chinese clients that it is evaluating adding production capacity for its powerful H200 AI chips. This comes after orders surpassed its current output level. NVIDIA (NVDA) Evaluates Adding Production Capacity for Powerful H200 AI Chips To provide a brief background, the US President Donald Trump recently announced that ...

Financial Data and Key Metrics Changes - NVIDIA reported revenue of $57 billion for Q3, representing a 62% year-over-year increase and a record sequential growth of $10 billion, or 22% [4] - GAAP gross margins were 73.4%, and non-GAAP gross margins were 73.6%, exceeding expectations [21] - Total revenue for Q4 is expected to be $65 billion, implying a 14% sequential growth [22] Business Line Data and Key Metrics Changes - Data center revenue reached a record $51 billion, up 66% year-over-year, with compute growing 56% driven by the GB300 ramp [5] - Networking revenue surged to $8.2 billion, up 162% year-over-year, with significant contributions from NVLink, InfiniBand, and Spectrum X Ethernet [14] - Gaming revenue was $4.3 billion, up 30% year-over-year, driven by strong demand for Blackwell [20] - Professional visualization revenue reached $760 million, up 56% year-over-year, attributed to the DGX Spark [20] - Automotive revenue increased to $592 million, up 32% year-over-year, primarily from self-driving solutions [20] Market Data and Key Metrics Changes - Analyst expectations for top cloud service providers' capital expenditures in 2026 have risen to approximately $600 billion, over $200 billion higher than at the start of the year [6] - The demand for AI infrastructure continues to exceed expectations, with NVIDIA's GPU-installed base fully utilized [4][5] Company Strategy and Development Direction - NVIDIA aims to capture a significant share of the estimated $3 trillion-$4 trillion annual AI infrastructure build by the end of the decade [4] - The company is focused on three platform shifts: from CPU to GPU computing, the rise of generative AI, and the emergence of agentic AI systems [24][25] - NVIDIA is committed to maintaining its leadership in AI computing and is actively engaging with U.S. and Chinese governments to navigate geopolitical challenges [11] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in achieving the $500 billion revenue forecast from Blackwell and Rubin by the end of calendar year 2026, with potential for additional orders [28] - The company is optimistic about the future, citing strong demand across various markets and the ongoing transition to accelerated computing and generative AI [6][24] - Management acknowledged challenges in the supply chain but emphasized their strong planning and partnerships to mitigate risks [52] Other Important Information - NVIDIA's partnerships with major companies like OpenAI and Anthropic are aimed at optimizing AI models for CUDA and expanding the ecosystem [18][43] - The company is preparing for significant growth, with inventory increasing by 32% quarter-over-quarter [21] Q&A Session Summary Question: Update on $500 billion revenue forecast for Blackwell and Rubin - Management confirmed they are on track for the $500 billion forecast and noted potential for additional orders beyond this figure [28] Question: Supply catching up with demand in the next 12-18 months - Management expressed confidence in their supply chain planning and noted that all applications are growing, which is driving demand for NVIDIA GPUs [30][31] Question: Assumptions on NVIDIA content per gigawatt in the $500 billion number - Management indicated that the content per gigawatt has increased with each generation, with Blackwell estimated at around $30 billion [34] Question: Role of AI ASICs or dedicated XPUs in architecture buildouts - Management emphasized that NVIDIA's architecture is uniquely positioned to handle the complexity of modern AI workloads, making GPUs the preferred choice [58]