Three AI Infrastructure Stocks Outpacing Nvidia in 2026 — With Momentum Heading Into 2027

Three AI Infrastructure Stocks Outpacing Nvidia in 2026

Nvidia (NASDAQ: NVDA) has cemented its status as the defining company of the AI era, but its 11% year-to-date gain in 2026 has quietly been overshadowed by a handful of smaller players deeper in the AI supply chain. Three companies — Monolithic Power Systems, Astera Labs, and Cadence Design Systems — have each posted stronger returns this year, and analysts suggest the structural forces driving their growth remain intact heading into 2027.

Monolithic Power Systems (NASDAQ: MPWR) — Power Management at the Heart of AI Data Centers

Monolithic Power Systems designs power management solutions that keep AI chips and servers running without interruption. As data centers scale up to handle increasingly demanding AI workloads, the need for stable, efficient power delivery has become a critical bottleneck — one that MPWR is well positioned to address.

Shares have climbed more than 40% year to date, reflecting accelerating demand from AI infrastructure operators. In the first quarter, revenue grew 26.1% year over year, with net income expanding at a slightly faster pace.

Two business segments are driving most of that growth. The enterprise data division — which delivers power management and integrated solutions specifically for AI chips and servers — represents roughly one-third of total revenue and nearly doubled year over year. The communications segment, focused on telecom infrastructure, satellite systems, and networking equipment, grew 55.5% year over year and 33.1% sequentially, now accounting for 14% of total sales.

Data suggests that as these two high-growth segments capture additional market share, overall revenue growth could accelerate further — a dynamic that investors in AI infrastructure plays will likely monitor closely.

Astera Labs (NASDAQ: ALAB) — Connectivity Solutions for the AI Server Stack

Astera Labs develops rack-scale connectivity hardware and software designed to speed up data transmission between AI chips and server clusters. Major hyperscalers have increasingly turned to the company for solutions that address one of the more technically demanding challenges in AI infrastructure: moving data fast enough to keep powerful chips fully utilized.

The company's growth trajectory has drawn significant attention. Revenue nearly doubled year over year in Q1, and a 14% sequential growth rate signals sustained momentum. For Q2, management guided toward a $360 million revenue midpoint — implying 16.7% sequential growth. Notably, the company has a history of exceeding its own guidance; it projected up to $297 million for Q1 but delivered $308.4 million.

The stock has nearly doubled in 2026, though it has pulled back approximately 33% from its recent peak. Performance indicators like these sequential growth patterns have historically preceded significant share price runs in the AI hardware space, as seen in the trajectory of other semiconductor-adjacent names like Micron Technology.

Cadence Design Systems (NASDAQ: CDNS) — Electronic Design Automation Anchored by a Record Backlog

Cadence Design Systems operates in a slightly different part of the AI ecosystem — electronic design automation (EDA) software and hardware. Its tools are used by chipmakers, including Nvidia and Advanced Micro Devices, to simulate, test, and validate semiconductor designs before they go into production at third-party foundries.

The stock is up nearly 20% year to date, a more moderate pace than its two peers, but the company's fundamentals tell a compelling story. Cadence reported a record $8 billion backlog and guided for 17% revenue growth across full-year 2026.

Looking ahead, the emerging agentic AI wave could represent a meaningful catalyst. Agentic AI systems require more sophisticated chips capable of handling complex, multi-step reasoning tasks — chips that need to be designed, tested, and validated using tools like those Cadence provides. This positions the company as a key checkpoint in the development pipeline for next-generation AI hardware, with potential implications for both revenue growth and profit margin expansion.

What Investors Are Watching

All three companies operate at different layers of the AI infrastructure stack — power delivery, data connectivity, and chip design — yet share a common thread: their growth is structurally tied to the ongoing buildout of AI computing capacity rather than to any single product cycle.

As hyperscaler capital expenditure continues to flow into AI infrastructure, the performance of these supporting players will likely remain a closely watched indicator of how broadly the AI investment cycle is distributing its gains beyond the most prominent names.