Technical Specifications
Intel Gaudi 2 Specifications
- 7nm process technology
- 24 Tensor Processor Cores (TPCs)
- 96GB of HBM2E with 2.45TB/s bandwidth
- 24x 100GbE RoCE V2 RDMA NICs
- Up to 40% faster than the previous-generation Gaudi accelerator in terms of price-to-performance
- Habana SynapseAI Software Suite
Why Choose the Intel Gaudi 2?
Leading the Charge in Next-Gen AI Workload Management
Performance
The Intel Gaudi 2 is twice as fast as the previous-generation Gaudi accelerator, and faster than the NVIDIA A100 GPU for inference workloads. This means that businesses and researchers can get more done with their AI workloads in less time, using less hardware.
Versitility
The Intel Gaudi 2 is a versatile and powerful AI accelerator that can be used for a wide range of deep learning workloads. It is ideal for businesses and researchers who need an AI accelerator that can handle a variety of tasks, from training complex models to running real-time inference.
Scalability
The Intel Gaudi 2 AI accelerator can be scaled using Ethernet, InfiniBand, and Habana SynapseAI software. This makes it a good choice for businesses and researchers who need to train and deploy large language models, computer vision models, and other deep learning models.
Use Cases
Training and deploying large language models
The Intel Gaudi 2 is a good choice for training and deploying large language models (LLMs), such as GPT-3 and LaMDA. LLMs are used for a variety of tasks, including generating text, translating languages, and answering questions in an informative way.
Developing and deploying computer vision models
The Intel Gaudi 2 can also be used to develop and deploy computer vision models for tasks such as image recognition, object detection, and video analysis. Computer vision models are used in a variety of industries, including retail, healthcare, and manufacturing.
Accelerating natural language processing (NLP) workloads
The Intel Gaudi 2 can also be used to accelerate NLP workloads, such as text classification, sentiment analysis, and question answering. NLP workloads are used in a variety of industries, including customer service, marketing, and finance.
Enabling real-time AI inference
Intel Gaudi 2 can also be used to enable real-time AI inference. This means that AI models can be used to make predictions on data as it is being collected, without having to wait for the data to be processed and stored in a database. Real-time AI inference is used in a variety of applications, such as self-driving cars, fraud detection, and medical diagnosis.
