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| Product Overview |
|---|
The Lenovo NVIDIA L4 24GB Tensor Core GPU provides a solid solution for AI inference and graphics acceleration. It fits well in setups demanding both memory capacity and processing power, including professional and enterprise environments where efficient parallel computing is essential. |
| General Information | |
|---|---|
| Brand | Lenovo |
| Part Number | 900-2G193-0200-200 |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| Bus Interface | PCIe Gen4 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.6, OpenCL, Vulkan: 1.3, CUDA |
| Memory | |
|---|---|
| Memory Size | 24GB |
| Interface | GDDR6 |
| Physical Characteristics | |
|---|---|
| Weight | 3.00 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL |
| Product Description |
|---|
The Lenovo NVIDIA L4 24GB Tensor Core GPU is built to handle demanding AI and graphics tasks with efficiency. Often found in data centers and professional workstations, this card is designed to accelerate AI inference, machine learning, and visualization workloads. Professionals working with complex models or high-resolution graphics benefit from its robust performance and large memory capacity. Key Features
This GPU is typically deployed in AI research labs, enterprise data centers, and visualization studios where high-throughput and reliability matter. It enables smooth handling of complex computations without slowing down workflows, making it a practical choice for teams that require consistent GPU performance. |
| Use Cases |
|---|
The Lenovo NVIDIA L4 24GB Tensor Core GPU is designed for environments requiring robust AI inference capabilities and high-throughput graphics processing. Ideal for enterprise IT infrastructures and data centers, it supports workloads that demand efficient parallel computing and large memory capacity for optimal performance. How It's Used:
This GPU supports operational efficiency by balancing memory capacity and processing power, enabling scalable deployments across diverse IT environments. Its architecture facilitates accelerated workloads, helping organizations maintain high performance under demanding computational tasks. |