NVIDIA T4 GPU
Data center deployments and AI inference workloads are the main uses for the adaptable and energy-efficient NVIDIA T4 GPU. The T4, in contrast to gaming or workstation GPUs, is designed to speed up cloud services, virtual desktops, video transcoding, and deep learning models. Due to its compact profile, great efficiency, and AI-centric capabilities, the NVIDIA T4 GPU, which was first introduced as part of NVIDIA’s Turing architecture portfolio, has been quite popular in the business sector.
Architecture
Turing architecture, the basis for consumer GPUs like the GeForce RTX 20-series, is also used to build the NVIDIA T4 GPU. But with an architecture that prioritizes inference over training, the NVIDIA T4 GPU is especially tailored for data centre settings.
- The Turing (TU104-based) GPU architecture.
- FinFET 12nm Process Node (TSMC).
- CUDA Cores: 2,560.
- (For mixed precision AI workloads) 320 Tensor Cores.
- RT Cores: Nonexistent (no support for ray tracing).
- Single-slot, low-profile form factor.
- Connectivity: PCIe Gen3 x16.
The NVIDIA T4 GPU‘s Tensor Cores are its most striking feature. They allow for high-throughput matrix operations, which makes the GPU ideal for AI applications such as recommendation systems, object identification, picture classification, and NLP inference.
Features
Because of its enterprise-grade characteristics, the NVIDIA T4 GPU is a great option for cloud AI services:
- Multiple precision levels are supported, including FP32, FP16, INT8, and INT4 modes, which balance performance and accuracy.
- TensorRT optimisation on NVIDIA for accelerating AI inference.
- Hardware engines NVENC and NVDEC provide effective video encoding and decoding (up to 38 HD video streams).
- Prepared for virtualization: supports virtual desktops and workstations with NVIDIA GRID.
- It works with the majority of workstations and servers since it is low profile and low power.
AI & Inference Performance
However, the NVIDIA T4 GPU is very strong for AI inference and is not designed for training large neural networks. It gives:
- A total of 130 INT8 performance tops.
- Performance of 65 TFLOPS for FP16.
- 8.1 Performance of FP32 TFLOPS.
Because of this, AI workloads can be processed in real time and at scale, which makes it perfect for applications like
- Inference from chatbots and NLP (BERT, GPT-style models).
- An examination of video content.
- Recognition of Speech and Pictures.
- YouTube, Netflix, and other services employ recommendation systems.
Because of its remarkable energy efficiency per dollar when used in hyperscale situations, the NVIDIA T4 GPU is well-liked by cloud providers such as Google Cloud, AWS, and Microsoft Azure.
You can also read NVIDIA RTX A5000 Price, Benchmark And Specifications
Gaming Performance
Out of curiosity, several developers and enthusiasts have explored the NVIDIA T4 GPU‘s capabilities even though it isn’t meant for gaming. Its limited gaming capabilities stem from the absence of display outputs and hardware features like RT cores (for ray tracing). But…
- Some contemporary games with medium settings can run on it at 1080p.
- GTX 1070 and GTX 1660 Super are comparable in terms of raw FP32 power.
- Neither Vulkan nor DirectX 12 Ultimate have been optimised for gaming.
Memory and Bandwidth
The T4’s memory is yet another crucial element of its operation:
- Type of Memory: GDDR6 16 GB.
- 320 GB/s of memory bandwidth.
- Internet Protocol: 256-bit.
The NVIDIA T4 GPU can efficiently handle heavy video workloads and AI models with its enormous memory capacity. Speed and cost are balanced with the GDDR6 memory.
Power and Efficiency
Power efficiency is one of the Tesla T4’s best qualities:
- 70 watts of TDP.
- Passive cooling, which depends on the server fan.
- Power Connectors: Not needed; PCIe slot power is used.
In congested situations, it is very deployable because to its low power consumption. The power and thermal issues that come with bigger GPUs like the A100 or V100 may be avoided by installing numerous T4s in a single server chassis.
Advantages
- Excellent AI inference capabilities in a simple form factor.
- Integrating it into current infrastructure is made simple by passive cooling and 70W TDP.
- Extensive support for AWS, Azure, and Google Cloud.
- It has enough GDDR6 memory (16 GB) to handle most inference jobs.
- Multi-precision support enables the best possible balance between accuracy and performance.
- Compatible with NVIDIA GRID and virtual GPU (vGPU) installations.
- It is beneficial in media pipelines due to its capability for video transcoding and AV1 decoding.
You can also read Intel Arc A770 GPU: The Ultimate Support For Gameplay
Disadvantages
- Limited FP32/FP64 throughput makes it unsuitable for training big deep learning models.
- Not ideal for gaming or content development, it lacks display outputs and ray tracing.
- PCIe Gen3 only (no compatibility for PCIe 4.0 or 5.0).
- Since there is no active cooling, enough server airflow is essential.
- Market availability for individual users is limited; it is often offered in bulk or through integrators.
NVIDIA T4 GPU Price
| Retailer/Platform | Price (USD) | Condition |
|---|---|---|
| NVIDIA Official (OEM bulk) | ~$1,299 | New (Enterprise) |
| Amazon | ~$1,000 – $1,200 | New/Used |
| Newegg | ~$1,050 | New |
| eBay | ~$700 – $1,000 | Used/Open-box |
| Server Integrators (e.g., Dell, HPE) | Varies (bundled) | OEM/Preinstalled |
NVIDIA T4 GPU Specifications
| Specification | Details |
|---|---|
| Architecture | Turing |
| GPU Model | TU104 |
| CUDA Cores | 2,560 |
| Tensor Cores | 320 |
| RT Cores | Not available (no ray tracing support) |
| Base Clock | 585 MHz |
| Boost Clock | 1,590 MHz |
| GPU Memory | 16 GB GDDR6 |
| Memory Interface | 256-bit |
| Memory Bandwidth | 320 GB/s |
| Interface | PCIe Gen3 x16 |
| Form Factor | Low-profile, single-slot |
| TDP (Power Consumption) | 70W |
| Cooling | Passive (requires good airflow in chassis) |
| FP32 Performance | ~8.1 TFLOPS |
| INT8 Inference | Up to 130 TOPS (with sparsity) |
| Mixed-Precision Support | FP16, INT8, INT4 via Tensor Cores |
| Virtualization Support | NVIDIA GRID, vGPU supported |
| Target Use Case | AI inference, ML workloads, cloud inferencing, virtual desktop infrastructure (VDI) |
| Display Outputs | None (headless) |
Final Thoughts
For today’s AI-driven data center, the NVIDIA T4 GPU is a small, effective, and powerful GPU. It excels in virtualization settings, video streaming, and machine learning inference. For scalable AI services, it continues to be one of the most popular corporate GPUs due to its low power consumption, excellent AI throughput, and extensive compatibility.
But it’s not made for content production, gaming, or general-purpose computing. A scalable and affordable option, the NVIDIA T4 GPU is ideal for businesses wishing to run recommendation systems, chatbots, or video analytics. Consumer RTX cards or the RTX A4000 are options that could provide more freedom for developers or consumers.
