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8-K2026-05-06· qwen-plus

SITM · SiTime Corporation

0001451809-26-000040

SEC filing

Summary

SiTime reported first quarter 2026 GAAP net loss of $5.2 million ($0.20 per diluted share) and non-GAAP net income of $38.9 million ($1.44 per diluted share), with revenue up 88.3% year-over-year to $113.6 million, driven by AI infrastructure demand and higher ASPs from differentiated Precision Timing platforms.

Key takeaways

Full analysis

SiTime’s Q1 2026 results reflect a significant acceleration in top-line growth and margin expansion, anchored by structural demand in AI infrastructure and high-performance systems. Revenue surged 88.3% year-over-year to $113.6 million — the highest quarterly revenue in company history — driven by increased adoption of its MEMS-based Precision Timing platforms across data centers, industrial robotics, and automotive applications. Management explicitly attributes this growth to 'system-level requirement' status for precision timing, enabling higher average selling prices and deeper customer engagement. Non-GAAP gross margin expanded 710 bps to 64.5%, reflecting product mix shift toward differentiated, higher-margin solutions and scaling efficiencies. Non-GAAP operating income jumped to $31.8 million (28.0% of revenue), up from $2.1 million a year ago, underscoring successful cost discipline despite $30.8 million in stock-based compensation — a deliberate investment in talent. The GAAP loss of $5.2 million includes $7.6 million in acquisition-related costs and $5.7 million in amortization of intangibles, both non-cash or one-time in nature. With $788.7 million in total liquid assets as of March 31, 2026, SiTime maintains exceptional financial flexibility. The concurrent inducement RSU grant to 42 new hires signals ongoing strategic hiring to support next-phase growth. While no formal forward guidance is provided in the release, management’s commentary emphasizes execution strength and confidence in sustained momentum, aligning with the broader industry trend of timing becoming mission-critical in AI and edge computing architectures.