#T4GPU

[govindhtech]

NVIDIA T4 GPU Price, Memory And Gaming Performance

NVIDIA T4 GPU

AI inference and data centre deployments are the key uses for the versatile and energy-efficient NVIDIA T4 GPU. The T4 accelerates cloud services, virtual desktops, video transcoding, and deep learning models, not gaming or workstation GPUs. Businesses use the small, effective, and AI-enabled T4 GPU from NVIDIA’s Turing architecture series.

Architecture

Similar to the GeForce RTX 20 series, the NVIDIA T4 GPU employs Turing architecture. Data centres benefit from the NVIDIA T4 GPU’s inference-over-training architecture.

TU104-based Turing GPU.

TSMC FinFET 12nm Process Node.

2,560 CUDA.

Mixed-precision AI workloads: 320 Tensor Cores.

No RT Cores (no ray tracing).

One-slot, low-profile.

Gen3 x16 PCIe.

Tensor Cores are the NVIDIA T4 GPU’s best feature. They enable high-throughput matrix computations, making the GPU perfect for AI applications like recommendation systems, object identification, photo categorisation, and NLP inference.

Features

The enterprise-grade NVIDIA T4 GPU is ideal for cloud AI services:

Performance and accuracy are balanced by FP32, FP16, INT8, and INT4 precision levels.

NVIDIA TensorRT optimisation for AI inference speed.

Efficient hardware engines NVENC and NVDEC encode and decode up to 38 HD video streams.

NVIDIA GRID-ready for virtual desktops and workstations.

It works with most workstations and servers because to its low profile and power.

AI/Inference Performance

The NVIDIA T4 GPU is well-suited for AI inference but not big neural network training. It provides:

Over 130 INT8 performance tops.

65 FP16 TFLOPS.

8.1 FP32 TFLOPS.

AI tasks can be processed in real time and at scale, making them ideal for applications like

Chatbot/NLP inference (BERT, GPT-style models).

A video analysis.

Speech/picture recognition.

Services like YouTube and Netflix use recommendation systems.

In hyperscale scenarios, the NVIDIA T4 GPU has excellent energy efficiency per dollar. Cloud providers like Google Cloud, AWS, and Microsoft Azure enjoy it.

Video Game Performance

Though not designed for gaming, developers and enthusiasts have studied the NVIDIA T4 GPU’s capabilities out of curiosity. Lack of display outputs and RT cores limits its gaming possibilities. But…

Some modern games with modest settings run at 1080p.

GTX 1070 and 1660 Super have similar FP32 power.

Vulkan and DirectX 12 Ultimate are not game-optimized.

Memory, bandwidth

Another important part of the T4 is its memory:

16 GB GDDR6 memory.

320 GB/s memory bandwidth.

Internet Protocol: 256-bit.

With its massive memory, the NVIDIA T4 GPU can handle large video workloads and AI models. Cost and speed are balanced with GDDR6 memory.

Efficiency and Power

The Tesla T4 excels at power efficiency:

TDP 70 watts.

Server fan-dependent passive cooling.

Use PCIe slot power; no power connectors.

Its low power usage makes it useful in busy areas. Installing multiple T4s in a server chassis can solve power and thermal difficulties with larger GPUs like the A100 or V100.

Advantages

Simple form factor with excellent AI inference.

Passive cooling and 70W TDP simplify infrastructure integration.

Comprehensive AWS, Azure, and Google Cloud support.

Its 16 GB GDDR6 RAM can handle most inference tasks.

Multi-precision support optimises accuracy and performance.

Compliant with NVIDIA GRID and vGPU.

Video transcoding and AV1 decoding make it useful in media pipelines.

See also Intel Arc A770 GPU: Ultimate Gameplay Support.

Disadvantages

FP32/FP64 throughput is too low for large deep learning model training.

It lacks display outputs and ray tracing, making it unsuitable for gaming or content creation.

PCIe Gen3 only (no 4.0 or 5.0 connectivity).

In the absence of active cooling, server airflow is crucial.

Limited availability for individual users; frequently sold in bulk or through integrators.

One last thought

The NVIDIA T4 GPU is tiny, powerful, and effective for AI-driven data centres. Virtualisation, video streaming, and machine learning inference are its strengths. Due to its low power consumption, high AI throughput, and wide compatibility, it remains a preferred business GPU for scalable AI services.

Content production, gaming, and general-purpose computing are not supported. The NVIDIA T4 GPU is perfect for recommendation systems, chatbots, and video analytics due to its scalability and affordability. Developers and consumers may have more freedom with consumer RTX cards or the RTX A4000.