Since their inception, GPUs have been the most powerful computing engines for accelerating compute workloads that drive innovations in business and science. The demand for GPU processing power is growing year on year with an expected CAGR of 33.6% by 2027, as per reports published by Allied Market Research.
It is fueled by comprehensive breakthroughs in machine learning (ML) and artificial intelligence (AI). Over the past two decades, as researchers pushed deep learning, neural networks, and other techniques to their limits, NVIDIA GPUs have proven especially adept at accelerating AI computations.
NVIDIA is focused on pushing the entire ML industry forward with a broad portfolio of hardware and software that enables faster, more accurate training and inference. Rather than thinking of AI as distinct stages—training, inference, and deployment, it is considered as a seamless pipeline.
To deliver breakthrough performance and efficiency for a broad range of enterprise workloads by an innovative accelerator, NVIDIA has introduced the A30 Tensor Core GPU to bring enterprise computing to its finest.
The A30 is targeted at mainstream enterprise servers and brings a new level of computing capability, efficiency, and enhanced ease of use for deep learning and scientific computing applications.
How is the NVIDIA A30 GPU becoming a game-changing solution?
Let’s take a look.
NVIDIA A30 GPUs are fueling the Next Wave of AI Innovation
Next-generation AI workloads demand higher performance while staying true to the power budget. NVIDIA’s A30 GPU has been powering innovation across all computing fields, making computations increasingly possible that were not before.
The number of applications continues to grow as developers are learning how to scale their applications on powerful GPU accelerators.
NVIDIA Ampere GPUs are built from the ground up for AI and hence offer superior performance for enterprise workloads at low power.
Let’s find out if that really is the case.
- Ideal for Deployments: Inside a low-power package of max 165 W, the A30 PCIe card combines third-gen Tensor cores with a considerable 24 GB of High-bandwidth memory (HBM2) and a high-speed memory bandwidth of 933 GB/s. This allows for maximized performance in mixed workloads that require both compute and data throughput capabilities, making it an ideal choice for deployments.
- Computation Accuracy: The A30 supports a wide range of floating-point values, including half-precision (FP16), single-precision (FP32), dual-precision (FP64), and the Brain Float architecture (BF16). It also supports unique precision levels, including integer (INT8) and TF32, which offer a single accelerator for every workload. By default, deep learning frameworks such as TensorFlow, PyTorch, and MXNet all run on TF32, and no code change is required to achieve faster performance over the previous generation Volta and Tesla architectures. These attributes speed up AI workloads, allowing networks to be both deep and narrow. These networks can take in more information from each layer because the precision required is achieved.
- Paramount GPU Utilization: The A30 also features Multi-instance GPU (MIG) capabilities, which allow you to maximize GPU utilization across large and small workloads while ensuring the quality of service (QoS). You can run four MIG instances on a single A30 at the same time, each with its personal streaming multiprocessor (SM), L2 cache, memory, decoder, and DRAM bandwidth. You can configure an A30 to have the following instances.
- One GPU instance – with 24 GB memory.
- Two GPU instances — each with 12 GB of memory.
- Three GPU instances — the first with 12 GB of memory and the other two with 6 GB of memory each.
- Four GPU instances — each with 6 GB of memory.
- Optimal Pairing via NVLink: The A30 supports PCIe Gen4 (64 GB/s) and 3rd-gen NVLink (Max 200 GB/s) for interconnection. It supports a single NVLink bridge with another A30, allowing two A30s to be paired to increase performance. For the optimum bridging performance and balanced bridge topology, wherever an adjacent pair of A30 GPUs is present in the server, the pair should be interconnected by an NVLink bridge that spans across 2 PCIe slots.
Moreover, A30 can outperform last-generation GPU in terms of performance delivered per dollar. Additionally, A30 supports full stack solutions viz. libraries, optimized deep learning models, deep learning frameworks like PyTorch, TensorFlow, and MXNet, and more than two thousand HPC and AI applications are available through NVIDIA GPU optimized NGC containers.
The A30 bulldozes last-generation GPUs in benchmarking tests
NVIDIA’s GPUs have set multiple records on an AI benchmark suite known as MLPerf. The suite covers a wide range of use cases, from object detection and image classification to recommendation systems and natural language processing (NLP).
In an earlier evaluation, the following six models from MLPerf Inference v1.1 were benchmarked to analyze A30’s performance improvement over the last-generation T4 and CPUs in which A30 demonstrated its outstanding potential.
- ResNet-50 v1.5 (Image Classification)
- SSD-Large ResNet-34 (Object Detection)
- 3D-Unet (Medical Imaging)
- DLRM (Recommender)
- BERT (NLP)
- RNN-T (Text-to-Speech)
The A30 is around 300 times faster than a CPU for the BERT language model. For inference using the above-mentioned six models, A30 delivers a performance increase of 3x to 4x over T4. The large memory size of A30 accounts for this performance increase.
These improvements enable a bigger batch size for models, and GPU memory bandwidth is almost three times faster than T4. As a result, data can be sent to computation cores in much shorter periods of time.
A30 is also capable of quickly pre-training AI models with TF32 and accelerating HPC applications with FP64 tensor cores. The tensor cores with TF32 in the A30 provide 10x better performance than T4 without any modifications to your code.
The automatic mixed precision increases the throughput by an additional two times, resulting in a combined 20x efficiency increase.
Also Read: How To Find The Best GPU For Deep Learning?
Driving Robust Performance in the Tensor Core Era
While the A30 leads in energy efficiency among its current peers, it is designed with performance in mind and loaded with features that help developers optimize their projects for maximum speed.
The following features of the A30 provide a stunning performance in helping NVIDIA attract the attention of the market.
- The A30’s 165W thermal design power enables the platform to deliver exceptional performance for industry-standard servers used in OTT platforms, computer vision, and conversational AI. GPU provisioning and managing is now easier than ever through NVIDIA’s VGPU (Virtual GPU) software.
- The A30 features four video decoders, one JPEG decoder, and one optical flow decoder to enhance intelligent video analysis (IVA). These capabilities enable the A30 to excel at video analytics and video processing. Consider these two factors before running a video analytics task and analyze how A30 outshines in these operations compared to its predecessor—the T4.
- Computational requirements of your model: This boils down to the GPU DRAM, tensor cores, and hardware parts that speed up the models to be trained or the frame preprocessing kernels.
- Encoding video streams prior to transmission: This reduces the amount of network bandwidth required. With the use of NVIDIA hardware decoders, these workloads can be accelerated further.
NVIDIA powered cloud servers from Ace Cloud Hosting
Ace offers servers powered by best-in-class NVIDIA Ampere series GPUs with resizable instances designed specifically for AI and HPC workloads. We have customizable cloud solutions that leverage NVIDIA’s high-end GPUs with prices ranging from $0.69/hr to $16/hr and more.
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Rely on our worldwide network of data centers, which are designed, constructed, maintained, and constantly monitored to meet your unique business needs. We offer simple subscription plans, and different compute instances with multiple price options no matter how big or small your requirements are.
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