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Azure Container Apps supports serverless GPU acceleration, enabling compute-intensive machine learning, and AI workloads in containerized environments. This capability allows you to use GPU hardware without managing the underlying infrastructure, following the serverless model that defines Container Apps.
This article compares the Nvidia T4 and A100 GPU options available in Azure Container Apps. Understanding the technical differences between these GPU types is important as you optimize your containerized applications for performance, cost-efficiency, and workload requirements.
Key differences
The fundamental differences between T4 and A100 GPU types involve the amount of compute resources available to the respective types.
| GPU type | Description |
|---|---|
| T4 | Delivers cost-effective acceleration ideal for inference workloads and mainstream AI applications. |
| A100 | Features performance advantages for demanding workloads that require maximum computational power. The extended memory capacity helps you work with large language models, complex computer vision applications, or scientific simulations that wouldn't fit in the T4's more limited memory. |
The following table provides a comparison of the technical specifications between the NVIDIA T4 and NVIDIA A100 GPUs available in Azure Container Apps. These specifications highlight the key hardware differences, performance capabilities, and optimal use cases for each GPU type.
| Specification | NVIDIA T4 | NVIDIA A100 |
|---|---|---|
| GPU Memory | 16GB VRAM | 80GB HBM2/HBM2e |
| Architecture | Turing | Ampere |
| Inference Performance | Cost-effective for smaller models | Substantially higher, especially for large models |
| Optimal Model Size | Small models (<10GB) | Medium to large models (>10GB) |
| Best Use Cases | Cost-effective inference, mainstream AI applications | Training workloads, large models, complex computer vision, scientific simulations |
Select a GPU type
Choosing between the T4 and A100 GPUs requires careful consideration of several key factors. The primary workload type should guide the initial decision: for inference-focused workloads, especially with smaller models, the T4 often provides sufficient performance at a more attractive price point. For training-intensive workloads or inference with large models, the A100's superior performance becomes more valuable and often necessary.
Model size and complexity represent another critical decision factor. For small models (under 5GB), the T4's 16GB memory is typically adequate. For medium-sized models (5-15GB) consider testing on both GPU types to determine the optimal cost vs. performance for your situation. Large models (over 15GB) often require the A100's expanded memory capacity and bandwidth.
Evaluate your performance requirements carefully. For baseline acceleration needs, the T4 provides a good balance of performance and cost. For maximum performance in demanding applications, the A100 delivers superior results especially for large-scale AI and high-performance computing workloads. Latency-sensitive applications benefit from the A100's higher compute capability and memory bandwidth, which reduce processing time.
If you begin using a T4 GPU and then later decide to move to an A100, then request a quota capacity adjustment.
Differences between GPU types
The type of GPU you select is largely dependent on the purpose of your application. The following section explores the strengths of each GPU type in context of inference, training, and mixed workloads.
Inference workloads
For inference workloads, choosing between T4 and A100 depends on several factors including model size, performance requirements, and deployment scale.
The T4 provides the most cost-effective inference acceleration, particularly when deploying smaller models. The A100, however, delivers substantially higher inference performance, especially for large models, where it can perform faster than the T4 GPU.
When looking to scale, the T4 often provides better cost-performance ratio, while the A100 excels in scenarios requiring maximum performance. The A100 type is specially suited for large models.
Training workloads
For AI training workloads, the difference between these GPUs becomes even more pronounced. The T4, while capable of handling small model training, faces significant limitations for modern deep learning training.
The A100 is overwhelmingly superior for training workloads, delivering up to 20 times better performance for large models compared to the T4. The substantially larger memory capacity (40 GB or 80GB) enables training of larger models without the need for complex model parallelism techniques in many cases. The A100's higher memory bandwidth also significantly accelerates data loading during training, reducing overall training time.
Special considerations
Keep in mind the following exceptions when you're selecting a GPU type:
Plan for growth: Even if you plan on starting with small models, if you expect to grow into needing more resources, consider starting with the A100 despite its higher initial cost. The continuity in your set-up might prove worth any extra costs you incur as you grow. Future-proofing like this is important to research organizations and AI-focused companies where model complexity tends to increase over time.
Hybrid deployments: Using both T4 and A100 workload profiles can help you split work into the most cost effective destinations. You might decide to use A100 GPUs for training and development while you deploy inference workloads on T4 GPUs.