artificial intelligence
Platforms for training and inference.
AI is reshaping how we interact with technology. To build smart, efficient systems, it’s key to separate the needs of training and inference.
Let’s About Artificial Intelligence Platforms.
Artificial Intelligence (AI) is a key part of the digital landscape, transforming our interactions with technology. When evaluating AI infrastructure, it’s crucial to separate the demands of the two main phases: training and inference.
Training.
This phase involves teaching the AI model to identify patterns and make predictions using large datasets. It requires massive computing power, typically provided by GPUs or TPUs, and has high energy demands due to intensive computations.
Inference.
This phase uses the trained model to make predictions or decisions based on new data. It requires less computational power but needs to be fast and efficient, especially for real-time applications like autonomous driving or voice recognition.
Timescale is Critical.
The duration of each phase is important: training an AI model can take many months, while the inference phase in production can last for years. For example, training ChatGPT-4 took at least four months, though the exact time is a corporate secret. Analysts estimate that training ChatGPT on a single GPU would take approximately 355 years.
In contrast, within the first week of its launch, ChatGPT gained over one million users and has been used by over 100 million people worldwide, receiving about 10 million daily queries.
Focusing on the infrastructure needed to efficiently deliver fast results in production is essential, as it’s the inference phase delivers business benefits.
Platforms for AI Training.
Hyperscale public clouds like Azure or AWS can offer flexible environments for research and development (R&D) of AI projects due to their scalability and cost-effectiveness.
During the R&D phase, businesses often need to experiment with different models, datasets, and configurations to find the optimal solution. Public cloud platforms provide the necessary computational resources on demand, allowing researchers to quickly scale up or down based on their needs. Azure and AWS both offer instances with NVIDIA A10G GPUsw hich are ideal for high-performance AI training
This flexibility is crucial for iterative experimentation and rapid prototyping, which are essential components of the R&D process. Additionally, public clouds offer a wide range of AI tools and services, such as pre-trained models and machine learning frameworks, which can accelerate development and innovation.
On-premises Options ↓
For running AI on-premises, the IBM Fusion hyper-converged appliance is an ideal platform due to its seamless integration of compute, storage, and networking resources. Designed to simplify the deployment and management of AI workloads, IBM Fusion HCI combines the power of Red Hat OpenShift with IBM’s advanced storage solutions. This integration allows businesses to efficiently manage containerised applications and data services, ensuring high performance and reliability. By leveraging IBM Fusion HCI, organisations can maintain control over their sensitive data, reducing the risks associated with public cloud environments while benefiting from the flexibility and scalability of a hybrid cloud approach.
IBM Fusion HCI is optimised for AI applications through its support for IBM’s watsonx platform, which enhances AI and data science capabilities. The appliance’s hyper-converged architecture ensures that AI workloads are processed with minimal latency and maximum efficiency, making it a robust solution for mission-critical applications.
Dell PowerEdge and Lenovo ThinkSystem ranges also offer GPU-based options. Both vendors offer powerful solutions tailored to meet the needs of AI training and machine learning applications.
Platforms for AI Inferencing.
Inferencing is the application of a trained model to make predictions or generate outputs based on new data. This phase is where the model is deployed in real-world scenarios, and its performance is critical to the success of AI-driven projects. Focusing on the infrastructure needed to efficiently deliver fast results in production is essential, as it’s the inference phase delivers business benefits.
One of the primary concerns with using hyperscale public cloud services for generative AI is the risk of sensitive information being leaked into the public domain. When businesses use public cloud services, they often have to share their data with third-party providers, which increases the risk of data breaches.
For example, if a company uses ChatGPT for generative AI, the final output becomes part of ChatGPT’s base of information, which is then used to train the next version of the model. This means that any sensitive information inputted into the system could potentially be exposed to other users.
For these reasons, on-premises infrastructure is particularly well-suited for inferencing due to its optimised processing capabilities and secure environment.
IBM Power 10 servers include a Matrix Math Accelerator (MMA) feature that significantly enhances AI inferencing by providing several key benefits:
- In-Core Acceleration: Directly integrated MMA units accelerate AI tasks, reducing the need for external GPUs and simplifying infrastructure.
- Optimised Libraries: Supports AI frameworks like PyTorch and TensorFlow for faster, more efficient inferencing.
- Energy Efficiency: Designed to be more energy-efficient than traditional GPU systems by optimising tensor operations.
- Reduced Precision Data: Handles 16-bit data, allowing simultaneous inference of more data without losing accuracy.
- Simplified Solution Stack: Minimises the need for additional device management and external accelerators, streamlining and reducing costs.
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