New GPU for Tesla M40 Tesla M60 Tesla M4 Graphic Card Deep Learning High-performance Computing GPU
Here’s a comparison table for the Tesla M40, Tesla M60, and Tesla M4 graphics cards based on their specifications and intended use cases. These GPUs are designed for data center, AI, and machine learning workloads rather than gaming.
| Feature | Tesla M40 | Tesla M60 | Tesla M4 |
|---|---|---|---|
| Release Year | 2015 | 2015 | 2016 |
| Architecture | Maxwell | Maxwell | Maxwell |
| GPU | GM200 | GM204 (Dual GPU) | GM206 |
| CUDA Cores | 3,072 | 4,096 (2,048 per GPU) | 1,024 |
| FP32 Performance | ~7 TFLOPS | ~4.8 TFLOPS (per GPU) | ~2.2 TFLOPS |
| Memory | 12 GB GDDR5 | 16 GB GDDR5 (8 GB per GPU) | 4 GB GDDR5 |
| Memory Bandwidth | 288 GB/s | 160 GB/s (per GPU) | 80 GB/s |
| TDP (Power Consumption) | 250 W | 300 W (total for dual GPU) | 50-75 W |
| Cooling | Passive cooling (requires external) | Passive cooling (requires external) | Active fan cooling |
| Form Factor | Full-height, dual-slot | Full-height, dual-slot | Low-profile, single-slot |
| Use Case | Deep learning, AI, HPC | Virtualization, cloud graphics | Edge inference, low-power workloads |
| FP16 Support | No | No | Yes |
| Target Market | Data centers, AI training | Virtual desktops, cloud rendering | Edge devices, inference workloads |
1. NVIDIA Tesla M40
The Tesla M40 is a high-performance GPU accelerator designed for deep learning and AI workloads. It features 12GB of GDDR5 memory and is built on NVIDIA’s Maxwell architecture. The M40 is optimized for training deep neural networks (DNNs) and is widely used in data centers for machine learning applications. With 3,072 CUDA cores and a power-efficient design, it delivers exceptional performance for AI research, image recognition, and natural language processing tasks.
2. NVIDIA Tesla M60
The Tesla M60 is a dual-GPU accelerator tailored for virtualized environments and graphics-intensive workloads. It combines two GPUs on a single board, each with 8GB of GDDR5 memory, and is based on NVIDIA’s Maxwell architecture. The M60 is ideal for virtual desktop infrastructure (VDI), rendering, and compute tasks, offering a balance of performance and efficiency for enterprise applications.
3. NVIDIA Tesla M4
The Tesla M4 is a low-profile, energy-efficient GPU accelerator designed for inference workloads and streaming applications. With 4GB of GDDR5 memory and based on NVIDIA’s Maxwell architecture, the M4 is optimized for real-time AI inference, video transcoding, and edge computing. Its compact design and low power consumption make it ideal for deployment in space-constrained environments.
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