The company expects its AI revenues to double to $4 billion this year, underscoring its leadership in the AI and networking markets.

Marvell's expanding AI chip pipeline includes collaborations with major companies like Microsoft on the Maia processor and Amazon on the Trainium3, enhancing its competitive position in the AI infrastructure market.

The company revealed two new compute-focused XPU projects from hyperscale clients, increasing its total design wins to 13, including a significant partnership with Meta.

Marvell reported strong Q1 financial results with revenue of $1.9 billion, marking a 63% year-over-year increase, and earnings per share that exceeded estimates.

Despite the day's gains, Marvell's stock has been volatile, down approximately 34.1% year-to-date, and trading significantly below its 52-week high.

Marvell Technology's stock surged over 7% on June 18, 2025, following the company's announcement of increased market projections for custom AI chips.

At a recent investor event, Marvell projected its total addressable market for data centers to reach $94 billion by 2028, reflecting a compound annual growth rate (CAGR) of 35% from 2023, an increase from the previously estimated 28%.

The company's total addressable market for AI chips has been raised from $43 billion to $55 billion by 2028, indicating significant long-term growth potential.

Positive Wall Street sentiment followed the announcements, with analysts reaffirming bullish ratings; for instance, Bank of America raised its price target for Marvell from $80 to $90.

Morgan Stanley's Joseph Moore acknowledged the ambitious nature of Marvell's plans while maintaining an Equal-Weight rating with a $73 price target, noting the potential to capture 20% of the newly sized $94 billion data center market.

Marvell aims for a 20% market share in both custom AI chips and the total data center market by 2028, positioning itself against competitors like Broadcom.

The new SRAM technology introduced by Marvell is expected to enhance the performance of AI chips, making them faster and more energy-efficient, which is crucial for companies developing large-scale AI systems.