Companies Dont Miss Out AI Transformation

Malaysia Get Ready – Pentech Solution Sdn Bhd Specialised in AI Infrastructure – AI Infrastructure for Data Centers 2024 – AI infrastructure refers to the underlying framework of hardware, software, and networking components required to support artificial intelligence (AI) and machine learning (ML) workflows. This infrastructure is essential for training, deploying, and running AI models efficiently and effectively.

At Pentech, we are a Data Center specialist providing AI Infrastructure (small/medium/large industries) to propel towards digital transformation where we help you to deploy your AI Infrastructure for Data Centers.

Main Components of AI Infrastructure: (Need to know)

1. Data Storage & Management
2. Compute Resources
3. Data Processing Framework
4. Machine Learning Framework
5. Machine Learning Operations (MLOps) Platform

Deep Learning	Deep learning is a subset of AI and ML that involves training neural networks with large amounts of data to make decisions or predictions without explicit programming. This provides a great advantage for your organization’s business models to be refined into better ones.
Predictive Modelling	Predictive modelling involves using statistical techniques and machine learning algorithms to predict future outcomes based on historical data. This helps the organization to make better and more accurate planning, or even countermeasures to elevate the business.
Analysis	AI enables advanced data analysis, allowing businesses to extract valuable insights from large, complex datasets. This may also bring the value of AI with progressive and intelligent reporting to make better plans as proactive measures.
Patterns & Recognition	AI systems can recognize patterns in data, images, and speech, enabling applications such as image recognition, natural language processing, and speech recognition. This helps an organization to save time and reduce human-related errors when there is a requirement for checking and identification.

Who should use it

Basically, any organization that deals with large datasets and find the needs on requiring automation, predictive analytics, and data-driven decision-making can utilize AI Infrastructure. This includes industries like banking, healthcare, manufacturing, retail, transportation, and more.

Basic requirements to begin with AI

Any organization that deals with large datasets and finds the needs requiring automation, predictive analytics, and data-driven decision-making can utilize AI Infrastructure. This includes industries like banking, healthcare, manufacturing, retail, transportation, and more.

An organization that needs to venture into AI needs access to quality data and expertise in data science and machine learning, meeting the requirements of computational resources by building and getting ready the AI Infrastructure.

5 Main Components of AI Infrastructure in terms of Hardware requirement:

1. CPUs (Central Processing Units): serve as the foundation and are essential within AI Frameworks. 
2. GPUs (Graphics Processing Units): with massive data processing requirements, GPUs are transformative for AI and well-needed for their parallel processing capabilities.
3. TPUs (Tensor Processing Units): a great innovation by Google designed to accelerate machine learning workloads and optimising both inference and training phases of AI development.
4. FPGA (Field-Programmable Gate Arrays): a type of configurable integrated circuit that is versatile and customizable for specific AI applications, including image recognition and natural language processing.  
5. Memory systems: one of the most important components that is crucial for storing and rapidly accessing vast amounts of AI application data to support the high-speed computation needs of AI algorithms.

Servers	Servers must be equipped with the 5 main components required for AI to cater the required computational power and memory capacity to train and deploy AI models efficiently. Power Consumption: High-demand of power consumption required by AI Infrastructure is unavoidable, with French powerhouse Schneider Electric estimating the power consumption of AI workloads is totalled around 4.3 gigawatts (GW), approximately 8% of the total power consumption of data centres in 2023. With the rise AI Infrastructure, energy-efficient hardware components and data centre infrastructure is highly sought-after to lower down power consumption to balance performance with sustainability.
Storage	High-capacity storage solutions (e.g. SSDs, NVMe, SCM) to store large datasets and model parameters. With the vast amount of data required to improve the AI stack, huge capacity and high-speed storage will always be in demand for building a good AI Infrastructure.
Network	High-speed networking infrastructure to facilitate data transfer between nodes in distributed computing environments. Here below are some of the key points: High Bandwidth: AI platforms today demand a minimum bandwidth of 800G per connectivity. Low Latency: To facilitate real-time processing and minimize delays, a low-latency network is crucial. Scalability: As AI efforts expand, the network must be able to scale accordingly to handle increased data flow and computational demands. Reliability: Networks must be reliable to ensure consistent delivery of AI computational results.

TRUSTED BRANDS

 

Here below are the brands of hardware providers that we are working closely with:

• Dell Technologies
• Hewlett-Packard Enterprise
• Pure Storage
• Lenovo
• SuperMicro

Pentech would like to recommend some of the top devices/components to build a data centre which can be equipped to run AI technology.

Brand	Components and Devices
Dell Technologies	Servers PowerEdge XE9680 PowerEdge XE9640 PowerEdge XE8640 PowerEdge R760xa Storage PowerScale/Isilon All Flash – F900, F710, F600, F210, F200, F800, F810 Archive – A300, A3000 Hybrid – H700, H7000 ECS EX500 EX5000 EXF900 ObjectScale XF960 Appliance Accelerator Options Intel- Data Center Max 1550 AMD- MI300X NVIDIA H100 A100 L40S L4 For more information, you can visit
Hewlett-Packard Enterprise	Servers ProLiant Gen11 servers (generally all designed for AI workloads) ProLiant DL320 Gen11 ProLiant DL380a Gen11 Storage GreenLake for File Storage Software Ezmeral Data Fabric Ezmeral Unified Analytics Machine Learning Data Management Purpose-Built Cray Supercomputing XD670
Pure Storage	AIRI (AI-Ready Infrastructure) o   NVIDIA OVX o   Cisco Validated FlashStack
Lenovo	Servers ThinkSystem SR680a V3 ThinkSystem SR685a V3 ThinkSystem SR780a V3 ThinkSystem SR675 V3 ThinkSystem SR650 V3 Hyperconverged ThinkAgile VX Series Storage ThinkSystem DG Series
Super Micro	Large Scale AI Training AI Rack Solutions Petabyte Scale Storage HGX H100 Systems HPC/AI HGX H100 Systems 8U SuperBlade 10 GPU Systems 1U Grace Hopper MGX Systems Enterprise AI Inference & Training 10 GPU Systems 6U SuperBlade 2U MGX System 2U Grace MGX System Visualization & Design Omniverse Optimized Systems 2U Hyper Systems Workstations

Pentech has been in the IT business for 18 years, with vast experiences in implementing and managing various IT infrastructure.

Over the years, we have established a strong foundation and continuous growth of our people, giving the best end-to-end experience for our customers, from initial sales engagement, all the way to project deliveries and managed services.

On the aspect of deployment, Pentech has established our own Project Management Framework, which is tailored to every different project deployments. This gives our customers a more efficient and seamless deployment experience.

To complete the experience, we have our Managed Services team in providing 24/7 NOC services, with predetermined SLAs on support services.

In the context of ESG, Pentech is always aligned with the efforts from all the vendors on pledging to reduce carbon footprint as best as we could, into all our solution designs. We aim for a better and greener future, fulfilling the sustainability mission.