AI is everywhere – in our phones, cars, and homes. This AI runs on something called inference. Inference is the process of using a trained AI model to make a decision, and this process needs a lot of power. The NVIDIA T4 GPU is optimized for this. It is a very efficient, well-suited, and powerful GPU, great for real-time AI applications and media streaming. Companies that need to run many AI tasks at once must consider the NVIDIA T4 GPU.
Let’s discuss more.
What is the NVIDIA T4 GPU?
The NVIDIA T4 is a data center GPU. It uses the NVIDIA Turing architecture. It is a universal accelerator. Moreover, its main job is to speed up AI inference.
It has a special design. It is a small, single-slot card. T4 consumes very little power and has special cores called Turing Tensor Cores. It also has 16 GB of fast GDDR6 memory. The T4 is not a GPU for training large AI models. It is built to run them efficiently in production. It is a perfect fit for servers in data centers. It is great for tasks like image recognition and language translation.
T4’s Key Features for Modern Workloads
The T4 GPU has some very useful features that make it a great choice for AI inference and more. They provide a big advantage for certain types of applications.
Multi-Precision Performance
The T4 GPU has 320 Turing Tensor Cores and 2560 NVIDIA CUDA cores. They are very flexible and can do calculations with different levels of precision. It can use FP32, FP16, INT8, and INT4 data types. This is very important for inference. Lower precision, like INT8 and INT4, can be much faster. The T4 can give up to 36x better inference performance than a CPU for some tasks that too without losing accuracy. This feature helps to run AI models much faster.
Low Power and Small Form Factor
The T4 is a very energy-efficient GPU. It has a typical power consumption of around 70 watts. It has a single-slot, low-profile design that makes it very easy to fit into any server. Also, it can be used in almost all data center environments. Its low power consumption helps to save a lot of money on electricity. This is a big advantage for large-scale deployments. Moreover, you can put many T4 GPUs in one server. This helps to get more performance from a single server.
Accelerated Media Processing
The T4 GPU is also great for video. It has dedicated hardware for video encoding and decoding. Further, it can decode up to 38 concurrent full-HD video streams (H.264/H.265). This feature is perfect for media streaming services and video analytics. Thus, you can use the T4 to analyze many video feeds in real time. This is useful for things like security systems and content delivery networks.
NVIDIA T4 vs. NVIDIA P4: A Comparison
The T4 GPU came after the P4 GPU and brought many key improvements. It is a much better choice for inference. Also, it has more memory and faster memory bandwidth. The addition of Tensor Cores gives it a huge speed advantage. The T4 also has a slightly lower power consumption. It is a very efficient and powerful upgrade from the P4.
This table shows some main differences between them.
| Feature | NVIDIA P4 | NVIDIA T4 |
| Architecture | Pascal | Turing |
| Process Node | 16nm | 12nm |
| Tensor Cores | No | Yes |
| Memory Capacity | 8 GB GDDR5 | 16 GB GDDR6 |
| Memory Bandwidth | 192 GB/s | 320 GB/s |
| Peak FP32 Performance | 5.5 TFLOPS | 8.1 TFLOPS |
| AI Performance (INT8) | 22 TOPS | up to 130 TOPS (using INT8 precision) |
| Power Consumption | 75 W | 70 W |
| Form Factor | Single Slot, Low Profile | Single Slot, Low Profile |
Cantech T4 GPU Services
Cantech provides top-notch GPU solutions for efficient AI inference. Get flexible and powerful services with NVIDIA T4 GPUs.
Cost-Effective Inference Solution
Our T4 GPU servers are very cost-effective as they provide excellent performance for the money. They are great for startups and small businesses, too. You can run many inference workloads without spending a lot. Moreover, our services help you to save on power and cooling costs too. This is a very smart way to deploy your AI applications.
Perfect for Media and Cloud
The T4 is perfect for media streaming. It is also great for cloud services. We offer T4-based solutions that can handle many video streams. Also, you can run your AI models on these streams. This is great for video analytics and transcoding. Our services help you to deliver a great user experience as we make sure your applications run smoothly with a 99.97% uptime guarantee.
Scalable and Easy to Use
Our T4 servers are very scalable. You can add more GPUs as your needs grow. We provide full root access to the servers and have complete control over your environment. We also give 24/7 technical support.
Conclusion
The NVIDIA T4 GPU is great for AI inference and media streaming. Its Turing architecture and Tensor Cores are very efficient. You get the strong performance with exceptional energy efficiency. All in all, it is a perfect choice for data centers and helps companies to run many real-time AI applications. The T4 is a smart and affordable solution for a wide range of workloads.
FAQs
What is the difference between AI training and AI inference?
AI training is the process of teaching an AI model. This needs a lot of data and powerful GPUs like the H100 or A100. AI inference is the process of using a trained model. It uses the model to make predictions. This needs a GPU that is fast and efficient. The T4 is designed specifically for this task.
What kind of applications are best for the T4 GPU?
The T4 is best for AI inference. This includes things like image classification, object detection, and speech recognition. It is also great for media processing. This includes video transcoding and real-time streaming. The T4 is not the best choice for training very large AI models from scratch.
Why is the low power consumption of the T4 so important?
Low power consumption is very important in a data center. It helps to reduce electricity bills and cooling costs. You can put more T4 GPUs in one server, and this increases the computing density. Thus, it gives you more performance per server rack. All in all, the T4 is one of the most energy-efficient GPUs.
How does the T4 accelerate video processing?
The T4 has a dedicated hardware encoder and decoder. This hardware can process video streams very quickly and offloads the work from the main GPU cores. Thus, the GPU focuses on other tasks. It can handle many video streams at the same time. This is why it is great for media services.
Is the T4 a good GPU for getting started with AI?
Yes, the T4 is a great GPU for getting started. It is relatively affordable compared to higher-end data center GPUs and can handle a wide range of inference and media tasks. You can use it to learn about AI inference and also deploy small to medium-sized models. Its low cost makes it very accessible for new users.
