Tag: GPU

At VMware Explore EU 2024, the session “AI Without GPUs: Using Your Existing CPU Resources to Run AI Workloads” showcased innovative approaches to AI and machine learning using CPUs. Presented by Earl Ruby from Broadcom and Keith Bradley from Nature Fresh Farms, this session emphasized the potential of leveraging Intel Xeon CPUs with Advanced Matrix Extensions (AMX) to run AI workloads efficiently without the need for GPUs.

Key Highlights:

1. Introduction to AMX:
   • AMX (Advanced Matrix Extensions), available in Intel’s Sapphire Rapids processors, enables high-performance matrix computations directly on CPUs, making them more capable of handling AI/ML tasks traditionally reserved for GPUs.
2. Why Use CPUs for AI?:
   • Cost Efficiency: Lower operating costs compared to GPUs.
   • Energy Efficiency: Ideal for environments where power consumption is a concern.
   • Sufficient Performance for Specific Use Cases: CPUs can efficiently handle tasks like inferencing and batch-processing ML workloads with models under 15-20 billion parameters.
3. Software Stack:
   • OpenVINO Toolkit: Optimizes AI/ML workloads on CPUs by compressing neural networks, improving inference performance with minimal accuracy loss.
   • Intel oneAPI: Provides a unified software environment for both CPU and GPU workloads.
4. Real-World Application:
   • Nature Fresh Farms: Demonstrated how AI-driven automation using CPUs effectively manages complex agricultural processes, including plant lifecycle control in greenhouses.

When to Choose CPUs Over GPUs:

• Inferencing and Batch Processing: When real-time responses are not critical.
• Sustainability Goals: Lower power consumption makes CPUs a viable option.
• Cost-Conscious Environments: For scenarios where reducing operational costs is a priority.

At VMware Explore EU 2024, the session “Unlocking Your Data with VMware by Broadcom and NVIDIA — RAG Deep Dive” delivered fascinating insights into the power of Retrieval Augmented Generation (RAG). Led by Frank Denneman and Shawn Kelly, this session explored how combining large language models (LLMs) with proprietary organizational data can revolutionize data utilization in enterprises.

What is RAG?

RAG combines the strengths of LLMs with a Vector Database to enhance AI applications by integrating them with an organization’s proprietary data. This synergy allows for more precise and context-aware responses, crucial for business-critical operations.

Why RAG Matters:

• Enhanced Accuracy: Unlike traditional LLMs prone to “hallucinations” or inaccuracies, RAG provides validated, up-to-date answers by sourcing information directly from relevant databases.
• Contextual Relevance: It seamlessly blends general knowledge from LLMs with specific proprietary data, delivering highly relevant insights.
• Traceability and Transparency: RAG solutions can cite the documents used to generate responses, addressing one of the significant limitations of traditional LLMs.

How RAG Works:

1. Data Indexing: Proprietary data is pre-processed and stored in a vector database.
2. Question Processing: When a query is made, it is semantically embedded and matched against the vector database.
3. Answer Generation: The most relevant data is retrieved and used to generate a precise answer.

Integration with NVIDIA:

NVIDIA’s Inference Microservice (NIM) accelerates this process by optimizing LLMs for rapid inference, leveraging GPU-accelerated infrastructure to enhance throughput and reduce latency.