NVIDIA T4 vs. NVIDIA A100 Comparison: Which GPU Should You Choose for AI and Data Center Workloads?
- server-parts.eu server-parts.eu
- Nov 2, 2024
- 3 min read
Updated: Apr 26
NVIDIA’s T4 and A100 GPUs are optimized for unique workloads in AI and HPC. The T4 excels in energy-efficient inference, while the A100 is built for powerful training and high-performance computing in data centers.
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This detailed breakdown covers every technical aspect to help you make an informed decision.
Quick Specs: NVIDIA T4 GPU vs. NVIDIA A100 GPU
Architecture and Core Comparison: NVIDIA T4 GPU vs. NVIDIA A100 GPU
Each GPU’s architecture caters to specific workloads:
NVIDIA T4 (Turing): With 2,560 CUDA cores and 320 Tensor Cores, the T4 balances power efficiency with moderate processing capabilities, ideal for real-time inference and lower power consumption. The 12nm process and 13.6 billion transistors support energy-efficient AI applications.
NVIDIA A100 (Ampere): Ampere’s 6,912 CUDA cores and 432 Tensor Cores provide high processing power for intensive AI and HPC tasks. The advanced 7nm lithography and 38 billion transistors offer better performance efficiency and power in data centers.
Key Insight: For high-throughput AI training, the A100’s Ampere architecture and additional Tensor Cores outperform the T4.
Performance Metrics: Floating-Point and Integer Precision: NVIDIA T4 GPU vs. NVIDIA A100 GPU
The A100’s significantly higher FP16 and INT8 performance makes it ideal for deep learning and AI model training. The T4 handles lighter inference tasks well but lacks the raw power of the A100.
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Memory and Bandwidth - VRAM Configuration and Throughput: NVIDIA T4 GPU vs. NVIDIA A100 GPU
With its high 4,096-bit bus width, the A100’s memory bandwidth handles large datasets and high-throughput applications, whereas the T4 is optimized for efficient, moderate data handling.
Power Efficiency and Cooling Requirements: NVIDIA T4 GPU vs. NVIDIA A100 GPU
The T4’s 70W TDP makes it suitable for edge computing and energy-limited setups. The A100 requires robust cooling due to its 260W TDP, often using liquid cooling in dense data centers.
Compatibility and Form Factor: NVIDIA T4 GPU vs. NVIDIA A100 GPU
The T4’s single-slot width and PCIe 3.0 compatibility make it highly versatile, while the A100’s PCIe 4.0 provides faster data transfers for next-gen servers.
API Compatibility: Supported 3D and Compute APIs: NVIDIA T4 GPU vs. NVIDIA A100 GPU
The T4’s broad API support, including Vulkan and OpenCL, adds flexibility for a variety of applications, while the A100 focuses more on CUDA-heavy data center workloads.
Pros and Cons Summary: NVIDIA T4 GPU vs. NVIDIA A100 GPU
NVIDIA T4:
Pros: Low power consumption, budget-friendly, versatile API compatibility, compact single-slot form factor.
Cons: Limited in high-precision and deep learning performance.
NVIDIA A100:
Pros: Exceptional AI training and HPC performance, high VRAM capacity, excellent memory bandwidth.
Cons: High power draw, typically requires advanced cooling solutions, higher price.011
Conclusion: Choosing Between NVIDIA T4 GPU and NVIDIA A100 GPU
The right GPU depends on your needs:
Choose the T4 for cost-effective inference, edge AI, and real-time applications.
Choose the A100 for advanced AI model training, HPC tasks, and data center environments requiring high performance and memory capacity.
NVIDIA T4 and NVIDIA A100 GPUs: Save up to 50%
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