NVIDIA (NVDA.US) is scheduled to announce its latest quarterly earnings and guidance on February 25th. Citigroup has issued a generally optimistic preview for the chip giant led by Jensen Huang, anticipating the company will provide strong forward-looking guidance. In a note to clients, Citi analyst Atif Malik stated that his model projects revenue for the January fiscal quarter to be approximately $67 billion, surpassing the Wall Street consensus estimate of $65.6 billion. Furthermore, he expects the company's revenue guidance for the April fiscal quarter to reach $73 billion, significantly higher than the market's expectation of $71.6 billion.

Malik indicated that the continued ramp-up of the B300 product line, combined with the introduction of the Rubin architecture, is expected to drive a year-on-year sales acceleration to 34% for NVIDIA in the second half of 2026. This represents a notable increase compared to the approximately 27% growth rate anticipated for the first half of 2026. He believes that the focus for most investors has already shifted beyond the immediate earnings report towards the annual GTC conference in mid-March. At this event, NVIDIA is expected to detail its inference roadmap, including how it plans to leverage Groq's low-latency SRAM intellectual property, and will likely provide its initial outlook for AI-related sales prospects for 2026 to 2027.

Based on this analysis, Malik maintains a "Buy" rating on NVIDIA with a price target of $270. Looking beyond short-term performance, Malik further noted that from a medium-to-long-term perspective, NVIDIA's current valuation appears "attractive." As visibility into 2026 performance improves, the stock has the potential to outperform the broader market in the second half of 2026. He also commented that the inference market is developing towards "greater diversification," which will offer more options for customizing model scale and application scenarios. This suggests that the use of AI accelerators will take on more varied forms. However, from a systems perspective, he expects NVIDIA to maintain its leadership position in both training workloads and those leaning towards inference and logical deduction. He considers MLPerf to remain the most valuable benchmark for comparing different AI accelerators.