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伺服器主機(jī)板
Server motherboards for demanding applications come in form factors: EEB/E-ATX/ATX/microATX/mini-ITX.
工作站主機(jī)板
Professionals that work in AI, animation, and data science rely on solid, scalable, and performant workstation boards.
機(jī)架式伺服器
Innovation and performance are in these optimal rack servers to be deployed in data centers with demanding applications.
GPU 協(xié)同運(yùn)算伺服器
Scalable parallel computing GPU dense servers that are built for high performance.
高密度伺服器
Compute, Storage, and Networking are possible in high density, multi-node servers at lower TCO and greater efficiency.
工作站
Stand-alone chassis for customers to customize and expand as needs change.
直接液體冷卻
Ensure peak performance in all configurations. Adopt direct-to-chip liquid cooling for the rising AI/HPC era.
浸沒式液冷
Address rising server heat with submerged cooling. Embrace next-gen liquid cooling for improved TCO and PUE.
OCP 資料中心伺服器
Based on OCP Open Rack Standards, barebone racks and nodes for datacenters.
x86 架構(gòu)伺服器
Tried and true x86 architecture based servers with support for the latest Intel and AMD processors.
人工智慧平臺(tái)
Processing power, large amounts of data, fast networking, and accelerators all bundle into a scale out ready HPC and/or AI server solution.
視覺特效演算
Systems that do visual applications from computer graphics to computer animation rely on visual computing servers.
高效能運(yùn)算伺服器
Processing power, large amounts of data, fast networking, and accelerators all bundle into a scale out ready HPC and/or AI server solution.
超融合伺服器
Software defined cluster of nodes can be dedicated for compute, storage, networking, or virtualization.
儲(chǔ)存伺服器
Capacity, reliability, and storage flexibility are built into these storage servers for enterprise and datacenters.
網(wǎng)路伺服器
Securely manage the use of files and applications for office environments while storing large amounts of data.
私有雲(yún)/混和雲(yún)伺服器
Server resources are effectively allocated via virtualization, and these servers are highly flexible.
邊緣運(yùn)算伺服器
Real time data processing at the source is required for edge computing with reduced latency for Internet of Things (IoT) and 5G networks as they use cloud.
e-Mobility
Telematics and Edge computing platform for Electric vehicle, Autonomous driving, ADAS, ITS, and V2X applications
