Piper Sandler names Nvidia a top pick, sees shares rising 20% after blockbuster year

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Piper Sandler names Nvidia a top pick, sees shares rising 20% after blockbuster year

Nvidia is a ‘most-own’ stock, according to Piper Sandler. The investment firm named the semiconductor giant its top large-cap pick, citing the launch of Nvidia’s upcoming Blackwell chip and lead within artificial intelligence computing. Analyst Harsh Kumar stood by his overweight rating on the stock and raised his price target to $175 from $140. Kumar’s updated price forecast is about 19% above where the stock closed Friday. Year to date, shares have soared 198%. NVDA YTD mountain NVDA YTD chart “Our viewpoint is rooted in the belief that the overall [total addressable market] for AI accelerators will continue to rise in 2025 by ~$70 billion, and we see NVDA well positioned to capture most of the incremental TAM increase while ceding only a small bit to its merchant chip competitors,” the analyst wrote. AI accelerators are components that make artificial intelligence and machine learning applications more efficient. Capital expenditure trends continue to be favorable at major tech companies, as seen in the latest reports from

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, the analyst said. Kumar thinks that these spending levels could be sustained through 2025. “We think this clearly benefits NVDA in the near to mid-term future and as such, we are taking our numbers up slightly for the January quarter and subsequent FY26 quarters,” the analyst added. Additionally, Nvidia’s Blackwell chip should become available in the first quarter of next year and could bring in between $5 billion to $8 billion in revenue, Kumar said. “As supply improves, we see more customers coming on for Blackwell beyond initial hyperscaler adoption in the following April quarter,” Kumar wrote. “Given expanded GPU allocations and initial shipments of the Grace Blackwell for inference applications, we are projecting Blackwell architecture revenues could increase to the tune of 200%+ in the April quarter following supply constraints.”

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