updates

Author: craigshoemaker

articles/container-apps/gpu-usage.md

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+---
+title: Comparing GPU types in Azure Container Apps
+description: Learn to how select the most appropriate GPU type for your container app.
+services: container-apps
+author: craigshoemaker
+ms.service: azure-container-apps
+ms.topic: how-to
+ms.date: 03/11/2025
+ms.author: cshoe
+---
+
+# Comparing GPU types in Azure Container Apps
+
+Azure Container Apps supports serverless GPU acceleration (preview), 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 essential for optimizing your containerized applications for performance, cost-efficiency, and workload requirements.
+
+## Key specifications and architectural differences
+
+The fundamental difference between T4 and A100 GPU types involves 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. The GPU is built on the Turing architecture, which provides sufficient computational power for most production inference scenarios. |
+| A100 | Features performance advantages for demanding workloads that require maximum computational power. The massive memory capacity (40 GB or 80GB of HBM2/HBM2e) 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.<br><br>For AI training, the A100 enables up to 2.5x faster model development compared to the T4. |
+
+## 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 up to 20 times faster than the T4. Additionally, the A100 introduces Multi-Instance GPU (MIG) technology, which allows a single GPU to be partitioned into up to seven isolated instances. This capability enables efficient resource sharing among multiple inference workloads, improving GPU use in multitenant environments.
+
+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 or when using MIG to serve multiple inference workloads simultaneously.
+
+### 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.
+
+For serious AI training workloads, especially with medium to large models, the A100 is the preferred choice. The T4 is best only when you're training small models in highly budget-constrained environments where performance isn't a primary concern.
+
+### Mixed precision and specialized workloads
+
+The capabilities for mixed precision and specialized compute formats differ significantly between these GPUs. The T4 supports FP32 and FP16 precision operations, providing reasonable acceleration for mixed precision workloads. However, its support for specialized formats is limited compared to the A100.
+
+The A100 offers comprehensive support for a wide range of precision formats, including TF32, FP32, FP16, BFLOAT16, INT8, and INT4. Since the A100 uses TensorFloat-32 (TF32), this GPU provides the mathematical accuracy of FP32 while delivering higher performance.
+
+For workloads that benefit from mixed precision or require specialized formats, the A100 offers significant advantages in terms of both performance and flexibility.
+
+## Selecting 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. Medium-sized models (5-15GB) can work on T4 with optimization techniques like quantization but generally perform better on A100. Large models (over 15GB) often require the A100's expanded memory capacity and bandwidth.
+
+Performance requirements should be carefully evaluated. 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.
+
+Power and space constraints must be considered in facility planning. The T4's 70W TDP is more power-efficient than the A100, allowing for more GPUs per server rack within the same power envelope. The A100 requires robust cooling and power infrastructure, especially for the 400-W SXM variant.
+
+Future scalability needs should factor into investment decisions. For short-term deployments or when working with stable workload sizes, the T4 might be sufficient. For long-term strategic investments or when anticipating growing model sizes and computational demands, the A100 provides capability for future workloads. Organizations requiring multi-GPU scaling should strongly consider the A100 with its NVLink capabilities, which enable more efficient scaling than what's possible with the T4.
+
+## 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.