Evaluating AWS GPUs
On AWS, customers have access to a range of GPU instances that are optimized for different workloads. These include the P4, P3, P2, DL1, Trn1, Inf2, Inf1, G5, G5g, G4dn, G4ad, G3, F1, and VT1 instances. Each of these instances has its unique characteristics and specifications, such as the number of cores, memory capacity, and memory bandwidth. Let's dive into detail for each of these instance types.
AWS P4 instances are designed to provide high performance for both Deep Learning training and inference workloads, featuring the NVIDIA A100 Tensor Core GPU based on the Ampere architecture. Compared to the previous Volta architecture, the Ampere architecture has more CUDA cores per streaming multiprocessor, new Tensor Cores for accelerating matrix operations, Multi-Instance GPU (MIG) for dividing GPU resources into multiple instances, Third-generation NVLink for high GPU-to-GPU communication, increased memory bandwidth, and improved energy efficiency.
P4 instances are well-suited for a wide range of compute-intensive workloads that require high performance, large VRM, and Memory Bandwidth, and can be used in industries such as financial services, healthcare, oil and gas, and more.
AWS P3 instances are based on the NVIDIA V100 GPU, which is based on the Volta architecture. Volta is an NVIDIA GPU architecture that was developed to provide improved performance and efficiency for data-intensive workloads such as machine learning, high-performance computing, and data analytics. Volta architecture has 5,120 CUDA cores per GPU, Tensor Cores for accelerating matrix operations, High-bandwidth memory (HBM2) for faster memory bandwidth and larger memory capacity, larger L1 cache, and support for NVLink for high-speed communication between multiple GPUs.
P3 instances are suitable for a wide range of compute-intensive workloads that require high performance and large amounts of VRAM and Memory Bandwidth and can be used in a variety of industries such as financial services, healthcare, oil, and gas. Compared to the latest architecture Ampere, Volta has fewer CUDA cores per streaming multiprocessor and less memory bandwidth.
AWS P2 instances are based on the NVIDIA Tesla K80 GPUs, which were launched in 2014 and were considered powerful at that time. However, newer generations such as the V100 or the latest A100 Ampere architecture provide more CUDA cores, memory bandwidth, and TFLOPS, making them more suitable for the latest and most demanding workloads. The Tesla K80 has 4,992 CUDA cores, 12 GB of GDDR5 memory, a memory bandwidth of 480 GB/s, a peak single-precision floating point performance of 2.91 TFLOPS, and a peak double-precision floating point performance of 870 GFLOPS. It also has a thermal design power (TDP) of 300 watts.
AWS P2 Instances are well-suited for a wide range of compute-intensive workloads that require high performance and large amounts of memory, such as machine learning, high-performance computing, and data analytics.
Amazon EC2 DL1 instances are powered by Gaudi accelerators, which are a family of AI accelerators developed by Habana Labs, a company that was acquired by Intel in 2019. Gaudi accelerators are designed for training and inference workloads in data centers and edge computing environments, they offer high performance for both training and inference workloads, large on-chip memory capacity, high memory bandwidth, power efficiency and support a variety of interfaces. However, they can be expensive and may not be cost-effective for small-scale deployment, not as scalable as cloud-based solutions, and have a limited ecosystem as compared to others like NVIDIA.
Gaudi accelerators are well suited for a wide range of AI workloads that require high performance and large amounts of memory, such as computer vision, natural language processing, and speech recognition, and can be used in a variety of industries such as financial services, healthcare, oil and gas, and more. It's worth noting that Gaudi's architecture is a new one, it may not have the same level of maturity and support that other architectures have.
Trn1 instances are based on AWS Trainium accelerators, which is the second-generation machine learning (ML) chip that AWS purpose-built for deep learning training. Trainium has been optimized for training natural language processing, computer vision, and recommender models used in a broad set of applications such as speech recognition, recommendation, fraud detection, image recognition, and forecasting. These instances are cost-effective, however, you will have to use the AWS Neuron SDK to compile models written in TensorFlow, PyTorch, and ONNX frameworks, which can be an extra effort.
Inf2 & Inf1
Inf2 instances are powered by AWS Inferentia2, the second-generation AWS Inferentia accelerator while Inf1 are powered by AWS Ingerentia1. Compared to Inf1 instances, Inf2 instances deliver 3x higher compute performance, 4x higher accelerator memory, up to 4x higher throughput, and up to 10x lower latency.
Like Tr1, these instances are cost-effective however one downside is that you will have to use AWS Neuron SDK to compile models written in TensorFlow, PyTorch, and ONNX frameworks which can be an extra effort.
G5, G5g, G4dn, G4ad, G3 Instances
Amazon EC2 G5, G5g, G4dn, G4ad, and G3 instances are different types of instances optimized for different use cases, such as compute-intensive workloads like machine learning and high-performance computing, cost-effective ML inference workloads, GPU-intensive workloads, general purpose workloads, and graphics-intensive workloads respectively. They are powered by the latest generation Intel Xeon processors and various NVIDIA and AMD GPUs, offering high memory-to-GPU bandwidth and large memory capacity, making them suitable for data-intensive workloads.
Amazon EC2 F1 instances are designed for Field Programmable Gate Array (FPGA) workloads, which are programmable hardware devices that can accelerate a wide range of workloads such as image and video processing, data compression, and machine learning. They are powered by the Xilinx UltraScale+ VU9P FPGA, high-performance Intel Xeon processors, large memory capacity, and high-speed networking. The FPGA on F1 instances can be programmed to perform custom logic, allowing users to optimize their workloads for specific use cases such as accelerating custom logic, video transcoding, and financial modeling.
These instances are an excellent choice for users who need to perform custom logic or accelerate specific workloads but require more flexibility than traditional instances, however, using F1 instances requires knowledge of FPGA programming which can be a barrier for some users.
Amazon EC2 VT1 instances are designed for video transcoding workloads, powered by Intel Xeon Scalable processors, and optimized for AV1 codec. They are suitable for use cases such as video streaming, post-production workflows, compression, and media and entertainment applications, providing high CPU power and memory to efficiently and quickly perform the task of converting video files from one format to another.
In summary, Amazon EC2 P4, P3, and P2 instances are all types of instances offered by Amazon Web Services (AWS) that are designed for different types of workloads.
- P4 instances are the latest generation of GPU instances, optimized for machine learning and high-performance computing workloads. They are powered by NVIDIA A100 Tensor Core GPUs and provide high memory bandwidth, high GPU memory capacity, and high-speed networking.
- P3 instances are also GPU instances, but they are powered by NVIDIA Tesla V100 GPUs. They are optimized for machine learning workloads and provide high memory bandwidth and high GPU memory capacity.
- P2 instances are also GPU instances, but they are powered by NVIDIA Tesla K80 GPUs. They are optimized for machine learning and high-performance computing workloads and provide high memory bandwidth and high GPU memory capacity.
- DL1 instances are powered by Gaudi accelerators from Habana Labs, which is an Intel company. They are optimized for deep learning workloads such as natural language processing, object detection, and image recognition. They are low-cost-to-train deep learning models.
- Trn1 instances are optimized for video transcoding workloads, they are powered by Intel Xeon Scalable processors and support AV1 codec.
- Inf2 and Inf1 instances are optimized for Inferentia-based machine learning inference workloads and provide high throughput and low latency.
- G5, G5g, G4dn, G4ad, and G3 instances are optimized for general-purpose workloads and provide high CPU power, high memory capacity, and high-speed networking.
- F1 instances are optimized for Field Programmable Gate Array (FPGA) workloads and provide high-performance Intel Xeon processors, large memory capacity, and high-speed networking.
- VT1 instances are optimized for video transcoding workloads and provide high CPU power, high memory capacity and support AV1 codec.
Each of these instances is designed for different use cases and workloads, and choosing the right one will depend on the specific requirements of your application or workload.
If you need further help in choosing the right AWS hardware for your ML workloads, please contact us at www.rayn.group and we would be happy to set up a consultation call with one of our AWS experts.