Sungrow Power Supply: Overseas Cloud Providers Highly Recognize SST Products, Expecting Small-Batch Prototypes in 2026
Sungrow Power Supply has indicated that Solid-State Transformers (SSTs) are an inevitable trend, primarily due to the fundamental challenges posed by the surge in AI computing power to data center power supply architectures. The company highlighted that SSTs are the optimal infrastructure solution for supporting the continuous expansion of AI computing power by eliminating intermediate multi-stage conversion losses. Overseas Cloud Service Providers (CSPs) have shown a high degree of recognition for Sungrow's SST products. The company plans to strengthen its experimental facilities to further enhance this recognition and solidify product reliability. Sungrow is actively engaging with leading North American cloud vendors to jointly define product specifications. The company anticipates that 2026 will focus on small-batch prototypes and experimental validation, including on-site testing with key clients. Mass production orders are expected to commence in 2027, with large-scale development beginning in 2028 and beyond, aligning with the expansion needs of next-generation computing chips.
The increasing demand for AI computing power necessitates significant advancements in data center infrastructure, particularly in power delivery efficiency. Sungrow's focus on Solid-State Transformers (SSTs) addresses a critical bottleneck by reducing energy losses in power conversion stages. The company's strategy of collaborating with major cloud providers on product definition and validation suggests a market-driven approach to technological adoption. The projected timeline, with initial small-batch production in 2026 and scaling in subsequent years, reflects the typical development cycle for complex, high-investment technologies in the enterprise sector. This transition to SSTs, if successful, could represent a substantial shift in power management for data centers, potentially impacting energy consumption and operational costs as AI workloads continue to grow exponentially.
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