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Product Overview |
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The Lenovo NVIDIA A30 24GB HBM2 Tensor Core Accelerator GPU combines large memory capacity with specialized tensor cores to accelerate AI and HPC workloads. It fits seamlessly into data center servers where performance and efficiency are critical, supporting a range of demanding computational tasks. |
General Information |
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| Brand | Lenovo |
| Part Number | 03GX237 |
Technical Information |
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| Chipset | Nvidia |
Memory |
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| Memory Size | 24GB |
| Interface | HBM2 |
Miscellaneous |
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| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL |
Physical Characteristics |
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| Slot Width | Dual-slot |
| Weight | 3.00 |
| Condition | Refurbished |
Product Description |
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The Lenovo NVIDIA A30 24GB HBM2 Tensor Core Accelerator GPU is built for demanding AI, machine learning, and high-performance computing tasks. It provides substantial compute power with its 24GB of high-bandwidth memory and specialized tensor cores, enabling faster data processing and model training. Often found in data centers and enterprise servers, this GPU is tailored for professional workloads that require reliable acceleration and efficient computation. Researchers, data scientists, and engineers benefit from its capability to handle complex algorithms and large datasets. Key Features
This GPU is regularly deployed in enterprise-grade servers powering AI research, data analytics, and scientific simulations. It delivers practical acceleration that helps reduce computation time and enhances productivity in professional settings. |
Use Cases |
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The Lenovo NVIDIA A30 24GB HBM2 Tensor Core Accelerator GPU is commonly deployed in high-performance computing clusters and enterprise data centers requiring efficient AI model training and inference. IT professionals and system architects use this accelerator to enhance computational throughput in environments with complex tensor operations and mixed precision workloads. How It's Used:
This GPU facilitates improved operational efficiency by delivering high memory bandwidth and tensor core performance, enabling scalable workload management across diverse data center architectures. Its integration supports sustained performance gains critical for demanding AI and HPC applications. |