Domestic AI Server Distributors

Key domestic distributors of AI servers include Dell, Hewlett Packard Enterprise (HPE), IBM, and ASI Partner, offering high-performance solutions for AI workloads.Major Domestic AI Server DistributorsDell Inc. – Dell provides a wide range of AI servers, including PowerEdge systems optimized for AI/ML workloads. They collaborate with Red Hat to ensure seamless deployment of AI models on Dell servers, supporting NVIDIA GPUs for high-performance training and inference . Hewlett Packard Enterprise (HPE) – HPE offers the ProLiant DL145 Gen11 and other AI-optimized servers, designed for edge and data center applications. Their solutions include integrated cooling, workload management software, and partnerships with ISVs for AI/ML applications . IBM – IBM delivers AI server solutions tailored for enterprise-scale AI workloads, including HPC and AI-optimized systems. Their servers support GPU acceleration and are integrated with software stacks for AI model deployment . ASI Partner – ASI specializes in custom AI server integration, offering NVIDIA Certified H100 HGX platforms, H200, and Grace Hopper Superchip systems. They provide direct liquid cooling solutions and versatile CPU/GPU configurations for large-scale AI inference and generative AI applications .Considerations for Choosing a Domestic DistributorServer Type: Rack-mounted, blade, or tower servers depending on deployment scale and density requirements .GPU/CPU Configuration: High-end NVIDIA H100/H200, AMD Instinct MI series, or Intel Gaudi accelerators for AI workloads .Cooling Solutions: Air cooling for smaller setups, liquid cooling for high-density AI clusters .Professional Services: Many distributors provide infrastructure management, software integration, and AI deployment support .SummaryFor domestic AI server procurement, Dell, HPE, IBM, and ASI Partner are leading distributors offering scalable, high-performance solutions. They provide a combination of hardware, software, and professional services to support AI training, inference, and analytics workloads, making them suitable for enterprises, data centers, and edge deployments .