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| Product Overview |
|---|
The NVIDIA Tesla M40 24GB GPU is built to accelerate deep learning training and high-performance computing tasks. It fits into server and workstation setups where large memory and fast processing are essential. This card helps users tackle demanding AI workloads by providing ample memory and efficient GPU architecture designed specifically for such environments. |
| General Information | |
|---|---|
| Brand | Nvidia |
| Part Number | NVIDIA Tesla M40-24GB |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| Bus Interface | PCIe 3.0 x16 |
| Supported APIs | OpenCL, CUDA |
| Output Interface | None |
| Memory | |
|---|---|
| Memory Size | 24GB |
| Interface | GDDR5 |
| Memory Bus | 384-bit |
| Physical Characteristics | |
|---|---|
| Slot Width | Dual Slot |
| Weight | 3.00 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Assembly Required | Yes |
| Eco Friendly | Yes |
| Product Description |
|---|
The NVIDIA Tesla M40 is a powerful GPU designed to accelerate deep learning and AI workloads. Often found in data centers and research labs, it helps speed up training for complex neural networks by leveraging its large memory and efficient architecture. It's built for professionals working with machine learning models, scientific simulations, and high-performance computing. Key Features
This GPU is typically deployed in server environments where intensive data crunching is required. It helps researchers and engineers cut down training times and handle bigger models than would be possible on standard GPUs. Whether in academic or enterprise setups, the Tesla M40 is a reliable workhorse for AI development. |
| Use Cases |
|---|
The NVIDIA Tesla M40 24GB GPU is predominantly utilized in enterprise data centers and research institutions where large-scale AI model training and complex simulations require extended GPU memory and high throughput. It benefits data scientists, AI researchers, and HPC professionals managing expansive neural networks or computational workloads demanding significant memory capacity. How It's Used:
By integrating the Tesla M40 into server infrastructures, organizations can improve computational throughput and memory-bound task efficiency, enabling scalable and sustained AI training cycles. Its architecture supports operational continuity in environments where maximizing GPU resource utilization directly impacts project delivery timelines and analytical depth. |