NVIDIA T4 GPU: Unlocking the Full Power of Scalable AI Inference
As artificial intelligence continues to reshape industries and redefine technological possibilities, organizations require infrastructure that can support rapid innovation, streamline deployment, and scale effortlessly. The NVIDIA T4 GPU is a purpose-built accelerator that delivers exceptional versatility, energy efficiency, and high-performance computing to meet the growing demands of AI-driven workloads.
Whether you’re deploying AI-powered customer experiences, real-time video analytics, or data center optimization tools, the NVIDIA T4 delivers performance, efficiency, and compatibility to help you innovate at speed and scale.
T4 GPU: The Engine Behind Next-Generation AI Inference
The NVIDIA T4 is built on the groundbreaking Turing architecture and equipped with 320 Turing Tensor Cores and 2,560 CUDA cores. This architecture enables the GPU to perform highly efficient inferencing across a wide spectrum of AI workloads, including deep learning, machine learning, computer vision, and natural language processing.
The T4 is uniquely designed to accelerate modern AI applications by supporting multi-precision compute from FP32 to INT4 enabling it to adapt to the performance and accuracy demands of any inference task. By leveraging NVIDIA’s software stack, including TensorRT, CUDA, and other AI development tools, developers can optimize their models for maximum throughput and responsiveness.
Key Technical Highlights
- GPU Architecture: NVIDIA Turing
- CUDA Cores: 2,560
- Turing Tensor Cores: 320
- Peak FP32 Performance: 8.1 TFLOPS
- Mixed-Precision (FP16/FP32): Up to 65 TFLOPS
- INT8 Throughput: 130 TOPS
- INT4 Throughput: 260 TOPS
- Memory: 16 GB GDDR6
- Memory Bandwidth: 300 GB/s
- Thermal Design: Passive cooling
- Max Power Draw: 70 W
- Interface: PCIe Gen 3.0 x16
- Form Factor: Low-profile, single-slot
Turing Tensor Cores: Powering Intelligent Inference
Modern AI is characterized by the explosion of complex neural network architectures from convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to generative adversarial networks (GANs), transformers, and hybrid models. The NVIDIA T4 GPU introduces Turing Tensor Cores capable of accelerating these diverse workloads using mixed-precision computing.
These cores empower the T4 to deliver significant performance improvements for inference while maintaining model accuracy. Whether processing large-scale recommendation engines or enabling real-time decision-making in autonomous systems, the T4 offers the programmable flexibility to support any AI pipeline.
By combining these cores with the NVIDIA TensorRT library, developers can fine-tune inference performance, achieving greater speed and energy efficiency without compromising predictive quality. With this powerful synergy between hardware and software, AI deployments become more scalable, cost-efficient, and effective.
Real-Time Inference at Scale
Real-time inference has become a cornerstone for modern digital services, from voice assistants and chatbots to fraud detection systems and dynamic content delivery platforms. Delivering instantaneous responses in these use cases requires GPUs with both low latency and high throughput.
The NVIDIA T4 features a multi-process service (MPS) engine that ensures efficient hardware resource allocation. This capability allows multiple workloads to be processed in parallel, reducing wait times and enhancing overall responsiveness. MPS is particularly effective in data center environments, where thousands of simultaneous inference requests need to be handled reliably.
By enabling concurrent processing with minimal latency, the T4 makes it possible to support real-time applications at massive scale all while maintaining consistent user experience and operational efficiency.
Enhanced Video Decode and Transcoding Capabilities
Video data continues to dominate internet traffic, driving demand for intelligent video processing. Applications like video surveillance, autonomous driving, smart retail, and media analytics rely heavily on accurate, real-time video decoding and interpretation.
The NVIDIA T4 provides industry-leading video processing capabilities, equipped with dedicated decode and encode engines. These engines deliver up to 2x the video decoding performance of previous generation GPUs. The T4 can decode up to 38 full HD streams concurrently and supports encoding in multiple resolutions including 720p, 1080p, and even Ultra HD (2160p).
Whether you’re delivering AI-enhanced video analytics or operating large-scale video streaming platforms, the T4 enables faster and more efficient video pipeline integration. Its intelligent resource allocation and performance tuning options including high-throughput and low-bit-rate modes help preserve video quality while maximizing bandwidth usage.
The Most Comprehensive AI Inference Platform
The T4 is more than just a powerful GPU it is a gateway to NVIDIA’s full-stack AI platform. This platform has matured over more than a decade and supports more than a million developers worldwide. From model training to deployment, NVIDIA offers an ecosystem of software tools and pre-optimized libraries designed to simplify the AI workflow.
NVIDIA TensorRT enables automatic optimization of AI models for inference, reducing compute resource consumption while accelerating performance. Libraries like cuDNN, CUTLASS, cuSPARSE, and DeepStream help accelerate core neural network operations, image and signal processing, and video analytics. Additionally, integration with Kubernetes and NVIDIA GPU Cloud (NGC) containers makes it easy to deploy and manage AI applications across on-prem and cloud infrastructure.
For teams looking to standardize and scale AI development, the T4 represents a reliable, proven platform that ensures compatibility with major deep learning frameworks and modern orchestration systems.
T4 for Data Scientists and Developers
For data scientists and AI engineers, the T4 significantly reduces the bottlenecks associated with model deployment and production inference. By supporting every major deep learning framework including TensorFlow, PyTorch, MXNet, and ONNX and integrating seamlessly with tools like TensorRT and Triton Inference Server, the T4 simplifies the transition from model training to high-performance inference.
Developers can also leverage mixed-precision computing to fine-tune performance and resource usage, striking the perfect balance between speed and accuracy. The result: faster time-to-insight, reduced compute costs, and accelerated AI product innovation.
T4 for IT Managers and Data Center Operators
From an infrastructure perspective, the NVIDIA T4 delivers excellent operational efficiency, helping organizations manage growing AI workloads with minimal cost and power consumption. Its low-profile, single-slot design and passive cooling make it ideal for dense data center deployments.
T4 GPUs are designed with energy efficiency and scalability in mind. Their support for multi-precision inference allows for standardization across a wide range of applications from real-time streaming to batch processing. Combined with NGC’s curated software containers and easy deployment mechanisms, T4 GPUs simplify maintenance and ensure uptime across mission-critical environments.
In short, the T4 reduces total cost of ownership (TCO) by enabling higher throughput with fewer servers and lower energy consumption a win-win for modern data centers.
Technical Specifications Overview
| Specification |
Details |
| GPU Architecture |
NVIDIA Turing |
| GPU Model |
TU104-895 |
| CUDA Cores |
2,560 |
| Tensor Cores |
320 |
| Base Clock |
585 MHz |
| Boost Clock |
1,590 MHz |
| Memory |
16 GB GDDR6 |
| ECC Support |
Yes (default enabled) |
| Memory Clock |
5,001 MHz |
| Memory Interface |
256-bit |
| Memory Bandwidth |
300 GB/s |
| Video CODECs Supported |
H.264, H.265 |
| 720p Encoding |
Up to 22 streams (HQ) |
| 1080p Encoding |
Up to 10 streams |
| 2160p Encoding |
2–3 streams |
| Power Consumption |
70 W |
| Cooling |
Passive |
| Operating Temp |
0–50°C |
| Humidity Range |
5%–90% RH |
| PCIe Interface |
Gen 3.0 x16 (also supports x8) |
| Form Factor |
Low-profile, single-slot |
| Dimensions |
6.61” x 2.71” |
| Compute APIs |
CUDA, TensorRT, ONYX, OpenCL |
| Graphics APIs |
DirectX 12, OpenGL 4.6, Vulkan 1.2 |
Operating System Compatibility
The T4 supports a wide array of operating systems, ensuring seamless integration into both enterprise and open-source environments:
- Windows Server 2012 R2, 2016, 2019
- Red Hat Enterprise Linux (7.7–7.9, 8.1–8.3)
- RedHat CoreOS 4.7
- SUSE Linux Enterprise Server (12 SP3+, 15 SP2)
- Ubuntu LTS (14.04, 16.04, 18.04, 20.04)
- Red Hat Linux 6.6+
Conclusion: A Smarter Future, Accelerated by T4
The NVIDIA T4 GPU is not just a technological advancement it’s a catalyst for AI transformation. It democratizes access to high-performance inference, empowers developers with a rich software ecosystem, and enables organizations to deploy smarter, faster, and more cost-effective AI solutions.
Whether you’re building the next-generation AI product, running scalable video analytics, or managing a high-throughput data center, the T4 is engineered to exceed expectations. It’s a future-proof choice for enterprises that are serious about unlocking the full potential of artificial intelligence.
