Habana

Utilize Intel® Gaudi® 3 AI accelerators, specifically designed for high-performance generative AI compute, via cloud instances or on-premise deployments.

Leverage the Intel® Gaudi® Software suite to port and optimize existing pre-trained generative models or build new ones from scratch.

Execute distributed training and fine-tuning operations across a cluster of Gaudi 3 accelerators, benefiting from the integrated 200 GbE for near-linear scalability.

Deploy the fine-tuned generative models for inference in production, leveraging Gaudi's efficiency for real-time content generation, summarization, or code completion services.

Deploy a cluster of Intel® Gaudi® 2 AI accelerators, known for their competitive performance in training large language models and strong price/performance on cloud platforms.

Configure the distributed training environment using the Intel® Gaudi® Software stack, integrating with popular deep learning frameworks.

Train complex models across multiple Gaudi accelerators, taking advantage of the integrated 200 GbE ports for high-bandwidth data exchange and near-linear scaling.

Monitor training progress, optimize hyperparameters, and iteratively refine models, ensuring efficient resource utilization and faster convergence.

Optimize trained deep learning models (e.g., for computer vision, natural language processing) using the Intel® Gaudi® Software for efficient inference on Gaudi accelerators.

Deploy these optimized models on Gaudi accelerators in data centers, edge locations, or cloud environments, integrating them with existing application pipelines.

Process incoming data streams with high efficiency, utilizing Gaudi's dedicated tensor processing capabilities to deliver real-time insights and responses.

Monitor inference performance and adjust deployment strategies to meet specific latency and throughput requirements of critical production services.

Train advanced medical imaging models (e.g., for stroke detection, COVID-19 diagnosis, tumor identification) using Intel Gaudi accelerators to accelerate model convergence.

Deploy the trained models on Gaudi accelerators within hospital data centers or secure cloud environments.

Integrate the AI system with Picture Archiving and Communication Systems (PACS) or Radiology Information Systems (RIS) for automated input of medical images.

Perform time-sensitive analyses, enabling faster detection and diagnosis, thereby improving patient outcomes and operational efficiency for healthcare providers.