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Product Overview |
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The IBM/NVIDIA Tesla T4 16GB graphics accelerator card provides robust GPU performance targeted at AI inference, machine learning, and data analytics. It fits well into server and data center environments needing efficient, high-throughput processing without a large power footprint. |
General Information |
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| Brand | IBM |
| Part Number | 900-2G183-0400-000 |
| Series | Tesla T4 |
Physical Characteristics |
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| Weight | 3.00 |
| Condition | Refurbished |
Product Description |
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The IBM/NVIDIA Tesla T4 16GB graphics accelerator card is built for demanding computational tasks like artificial intelligence, machine learning, and data analytics. It’s commonly used in data centers and server farms where processing large datasets quickly is crucial. Often found in enterprise environments and research labs, this GPU helps professionals accelerate their workloads without consuming excessive power. Its efficient design balances performance with thermal management, making it a practical choice for high-density setups. Key Features
This accelerator is typically deployed in cloud servers and AI inference nodes where real-time data processing matters. It plays a critical role in speeding up computations without requiring extensive hardware changes. In practice, users benefit from its balance of power and efficiency, which helps maintain smooth operations even under heavy computational loads. |
Use Cases |
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The IBM NVIDIA Tesla T4 16GB GPU accelerator is designed for deployment in data centers and enterprise server environments where AI inference, machine learning, and real-time analytics are critical. Organizations handling large volumes of data and requiring scalable GPU compute within dense server racks gain significant benefits from its compact form factor and efficient power usage. How It's Used:
This GPU accelerator facilitates operational efficiency by delivering high-throughput parallel processing within limited power envelopes. Its scalability and compatibility with PCI-E 3.0 x16 slots make it suitable for integrating into existing server architectures, improving workload performance without extensive infrastructure changes. |