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Product Overview |
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The 723KD Dell AMD Radeon Instinct MI100 combines a large 32GB HBM2 memory pool with PCIe 4.0 x16 connectivity to support demanding computational workloads. It fits well in professional computing environments focused on AI, machine learning, and scientific research, offering robust memory bandwidth and efficient processing for complex tasks. |
General Information |
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| Brand | Dell |
| Part Number | 723KD |
Technical Information |
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| Chipset | AMD |
| Bus Interface | PCI Express 4.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.6, OpenCL, OpenCL: 3.0, Vulkan: 1.2.175 , DirectCompute |
| Output Interface | None (compute accelerator — no display outputs) |
Memory |
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| Memory Size | 32GB |
| Interface | HBM2 |
Physical Characteristics |
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| Weight | 3.00 |
| Condition | Refurbished |
Miscellaneous |
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| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL |
Product Description |
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The 723KD Dell AMD Radeon Instinct MI100 is a professional-grade graphics card built for heavy computational tasks rather than traditional gaming. It’s often found in data centers and research labs where AI training, machine learning, and complex simulations demand large memory and fast compute speeds. Engineered for professionals and researchers, this GPU accelerates workloads that require intense parallel processing power and high memory bandwidth. It’s commonly used in environments running HPC applications, scientific modeling, and deep learning frameworks. Key Features
This GPU is typically deployed in server racks or high-performance compute stations where workload reliability and sustained performance are critical. It plays a key role in accelerating data-intensive computations, helping researchers and engineers complete complex analyses faster. |
Use Cases |
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The Dell AMD Radeon Instinct MI100 GPU is ideal for deployment in high-performance computing clusters and data centers where AI training and complex simulations demand substantial memory and processing power. Enterprises and research institutions running mixed precision workloads will find this GPU suitable for accelerating scientific computations and large-scale machine learning models. How It's Used:
This GPU supports operational efficiency by delivering high throughput and memory capacity required for scalable compute tasks, enabling organizations to optimize resource allocation and reduce time-to-insight in demanding computational fields. |