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Product Overview |
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The Dell AMD Radeon Instinct MI100 32GB GPU is tailored to accelerate complex computations in data centers and research labs. It fits into high-performance server setups and is used primarily for AI, machine learning, and scientific tasks that require large memory and fast processing capabilities. |
General Information |
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| Brand | Dell |
| Part Number | 0723KD |
Technical Information |
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| Chipset | AMD |
| Bus Interface | PCIe 4.0 x16 |
| Supported APIs | OpenCL, OpenCL: 3.0 |
| Output Interface | None |
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 Dell AMD Radeon Instinct MI100 is a powerful graphics card built for demanding computational tasks such as machine learning, scientific simulations, and data analytics. It is often found in data centers and research facilities where high-performance computing is essential. Professionals and developers benefit from its large memory capacity and rapid data throughput, which help accelerate complex workflows. Key Features
This GPU is typically deployed in enterprise-level computing clusters and supercomputers, powering research projects and AI development. Its robust architecture enables consistent performance under sustained heavy loads, making it a reliable choice for intensive compute environments. |
Use Cases |
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The Dell AMD Radeon Instinct MI100 32GB GPU is primarily deployed in high-density data center racks and HPC clusters where large-scale AI model training and scientific simulations demand extensive memory and compute throughput. Organizations managing complex machine learning pipelines and research institutions benefit from its ability to handle memory-intensive workloads efficiently. How It's Used:
This GPU enables scalable performance by integrating seamlessly into existing data center architectures, optimizing throughput for demanding compute loads. Its design supports efficient resource utilization and accelerates processing timelines for complex AI and HPC workloads. |