About Gigabyte G294-A22-AAP2 AI GPU Server
The Gigabyte G294-A22-AAP2 AI GPU Server is a high-performance 2U GPU server designed for artificial intelligence, machine learning, deep learning, data analytics, and high-performance computing (HPC) applications. Powered by dual AMD EPYC™ 9005/9004 Series processors, the server delivers exceptional compute capability and scalability for enterprise AI environments. With support for up to four dual-slot PCIe Gen5 GPUs, DDR5 memory, high-speed storage, and advanced expansion capabilities, the Gigabyte G294-A22-AAP2 AI GPU Server provides an ideal platform for GPU-accelerated workloads.
Gigabyte G294-A22-AAP2 AI GPU Server in Bangladesh
The Gigabyte G294-A22-AAP2 AI GPU Server is suitable for AI research centers, cloud service providers, universities, enterprise data centers, and organizations in Bangladesh seeking powerful GPU computing infrastructure for AI training and inference.
User Experience
Designed for 24/7 enterprise operation, the server combines AMD EPYC processing power, PCIe Gen5 expansion, and high-bandwidth DDR5 memory to ensure stable performance for demanding AI and HPC workloads.
Why Choose Gigabyte G294-A22-AAP2 AI GPU Server?
The Gigabyte G294-A22-AAP2 AI GPU Server offers a balance of CPU performance, GPU scalability, storage flexibility, and enterprise-grade reliability. Its 2U form factor enables high compute density while maintaining efficient cooling and power delivery.
Key Features
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Supports up to 4 x Dual-Slot PCIe Gen5 GPUs
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Dual AMD EPYC™ 9005/9004 Series Processors
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12-Channel DDR5 RDIMM Architecture
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24 x DDR5 DIMM Slots
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Dual ROM Architecture
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PCIe Gen5 Expansion Technology
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High-Speed NVMe Storage Support
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OCP NIC 3.0 Expansion Support
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ASPEED® AST2600 BMC Remote Management
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Enterprise-Class 2U Rackmount Design
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Redundant Power Supply Configuration
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Optimized for AI, HPC, and Data Center Workloads
Performance Advantage
The combination of dual AMD EPYC processors, PCIe Gen5 connectivity, and GPU acceleration allows the server to handle AI model training, deep learning, data analytics, virtualization, and scientific computing workloads efficiently.
Where It Can Be Used?
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AI Training
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AI Inference
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Machine Learning
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Deep Learning
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Large Language Models (LLMs)
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High-Performance Computing (HPC)
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Scientific Research
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Data Analytics
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Cloud Computing
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Enterprise Virtualization
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Financial Modeling
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Healthcare Research
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Engineering Simulations
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Enterprise Data Centers