Blog chia sẻ kiến thức FPT Cloud

Two AI Factories developed by FPT Corporation have been listed in the latest global supercomputer ranking TOP500, affirming FPT’s world-class capabilities in artificial intelligence (AI) and cloud computing. The TOP500 is the world’s most recognized ranking of high-performance computing systems, based on the LINPACK benchmark, which measures the number of complex floating-point operations per second (FLOPS) that a system can perform. Established in 1993 by three renowned HPC researchers and published twice a year, the TOP500 List is widely recognized by governments, scientific institutions, and enterprises as the global standard for not only advanced hardware capabilities but also excellence in system design, optimization, and the ability to power complex AI and scientific workloads at scale. In the June 2025 edition of the TOP500, FPT's AI factories, located in Japan and Vietnam, are placed No. 36 and No. 38, respectively. This ranking positions them among the world's top supercomputing infrastructures and recognizes FPT as the No.1 commercial AI cloud provider in Japan, offering NVIDIA H200 Tensor Core GPUs (SXM5). [caption id="attachment_63304" align="aligncenter" width="800"] FPT's AI Factories ranked 36th and 38th in the TOP500 List (Source: TOP500.org)[/caption] The Japan-based AI factory, boasting 146,304 cores, achieved a remarkable performance of 49.85 PFLOPS. Meanwhile, AI Factory in Vietnam attained 46.65 PFLOPS with 142,240 cores. Both AI factories employ InfiniBand NDR400, enabling seamless scaling from a single GPU to clusters of over a hundred nodes in each region, delivering consistently high performance and low latency for large-scale AI and HPC workloads. The inclusion in the 65th edition of the prestigious TOP500 list recognizes FPT’s AI factories globally for their exceptional computing power, engineering expertise, and service quality, demonstrating their readiness to meet the global demand for AI research, development, and deployment. This achievement also positions Vietnam among the world’s top 15 AI nations, alongside the United States, China, Japan, Germany, and France, and stands as a testament to FPT’s ongoing efforts to enhance the country’s global tech presence. FPT has announced plans to establish three more AI Factories globally within the next five years, contributing to Vietnam’s ambition to lead the region in AI computing infrastructure. “Developed under the philosophy of ‘Build Your Own AI’, FPT AI Factory is not merely a leap in high-performance computing infrastructure, but also a solution to a core market bottleneck: making AI more accessible and applicable across all areas of life. With FPT AI Factory, any organization, business, or individual in Vietnam, Japan, and all over the globe can develop AI tailored to their own needs, gain unique competitive advantages, and accelerate comprehensive digital transformation,” said Mr. Le Hong Viet, CEO of FPT Smart Cloud, FPT Corporation. [caption id="attachment_63358" align="aligncenter" width="800"] FPT’s multi-region AI factory is globally certified, demonstrating readiness to accelerate AI innovation worldwide (Source: FPT)[/caption] Launched in November 2024, FPT AI Factory has been chosen by leading tech companies, such as LandingAI, to build advanced AI solutions that deliver real-world impact. FPT Corporation (FPT) is a leading global technology and IT services provider headquartered in Vietnam. FPT operates in three core sectors: Technology, Telecommunications, and Education. As AI is indeed a key focus, FPT has been integrating AI across its products and solutions to drive innovation and enhance user experiences within its Made by FPT ecosystem. FPT is actively expanding its capabilities in AI through investments in human resources, R&D, and partnerships with leading organizations, including NVIDIA, Mila, and AITOMATIC. These efforts are aligned with FPT's ambitious goal to solidify its status among the world's top billion-dollar IT companies.  For more information, please visit https://fpt.com/en.

I. Introduction Large Language Models (LLMs) have transformed artificial intelligence by enabling machines to understand and generate human-like text. These models are initially pre-trained on vast datasets to grasp general language patterns. However, as new information emerges, like scientific discoveries or trending topics, models can become outdated. Continual pretraining addresses this by updating pretrained LLMs with new data, avoiding the need to start from scratch.  This blog post dives into continual pretraining, exploring its mechanics, challenges, and benefits. We’ll also show how FPT AI Studio supports this process through a practical experiment. As continual pretraining demands significant compute resources and streamlined workflows, having the right platform is critical.  Built on the NVIDIA-powered FPT AI Factory, FPT AI Studio provides an unified platform with flexible GPU options, built-in security, and zero infrastructure setup. These capabilities make it easier and faster to run complex training workflows at scale.  By the end, you’ll understand why continual pre-training is essential and how FPT AI Studio can help keep LLMs adaptable and relevant. II. Continual Pretraining in LLMs 1. What Is Pretraining for LLMs?  Pre-training is the foundation of LLMs, where models are trained on massive, diverse datasets like web texts, books, or articles. This process helps them learn language structure and semantics. By predicting the next word, models leverage vast unlabeled data. The result is a versatile model ready for tasks like chatbots or content generation. 2. Pretraining Challenges Computational Resources: Training requires thousands of GPUs, consuming significant energy and funds.  Data Quality: Datasets must be diverse and unbiased to avoid skewed outputs, which can raise ethical concerns.  Scalability: Managing large datasets and models is complex, demanding efficient systems.  Obsolescence: Pretrained models can quickly become outdated as new knowledge emerges. 3. From Pretraining to Continual Pretraining Traditional pretraining is a one-time effort, but the world doesn’t stand still. New trends, research, and language patterns emerge constantly. Continual pretraining updates the models incrementally, allowing them to adapt to new domains or information without losing existing knowledge. This approach saves resources compared to full retraining and keeps models relevant in dynamic fields like medicine or technology. 4. What Is Continual Pretraining? Continual pretraining involves further training a pretrained LLM on new or domain-specific data to enhance its knowledge. Unlike fine-tuning, which targets specific tasks, continual pretraining broadens general capabilities. It uses incremental learning to integrate new data while preserving prior knowledge, often through techniques to balance retention and adaptation. For example, a model might be updated with recent news or scientific papers to stay current. 5. Continual Pretraining Challenges Catastrophic Forgetting: New training can overwrite old knowledge, reducing performance on previous tasks.  Data Selection: Choosing high-quality, relevant data is critical to avoid noise or bias.  Model Stability: Models must remain robust, necessitating careful monitoring. 6. Use Cases Continual pretraining shines in various scenarios:   Domain Adaptation: Continual pretraining allows these models to be further trained on domain-specific corpora, such as clinical notes, legal contracts, or financial reports, thereby enhancing their ability to understand and generate more accurate, relevant, and trustworthy content in those areas.   Knowledge Updates: Language models trained on static datasets can quickly become outdated as new events unfold, technologies emerge, or scientific discoveries are made. Continual pretraining enables periodic or real-time integration of up-to-date information, keeping the model aligned with the latest developments. This is especially useful for any task where current knowledge is essential.  Multilingual Enhancement: Many language models initially support only a limited set of widely spoken languages. Continual pretraining provides a pathway to extend these models with the low-resource languages, regional dialects, or even domain-specific jargon within a language. This ensures broader accessibility and inclusiveness, making the technology usable by a more diverse global population. 7. Why Not Just Fine-Tune? Fine-tuning focuses on instruction-tuning the model across a series of downstream tasks that may differ in data distribution or change over time. This typically uses labeled datasets, such as question-answer pairs, to guide the model toward performing specific, well-defined tasks more effectively.  Fine-tuning adapts models for specific tasks but has limitations:   Task-Specificity: It may not generalize to broad knowledge updates.   Overfitting Risk: Models can overfit to small datasets, losing versatility.  III. Continual Pretraining on FPT AI Studio  With growing interest in Vietnamese LLMs, we conducted a real-world continual pretraining experiment using FPT AI Studio — a powerful no-code platform developed by FPT Smart Cloud. FPT AI Studio provides a streamlined platform for managing and executing LLM Training workflows, including the continual pretraining of LLMs. Its advantages include:  A user-friendly graphical interface for pipeline creation and management.   Integrated data management through Data Hub, allowing easy connection to S3 buckets to upload the large dataset.   Simplified configuration of computing resources and hyperparameters.   Address any difficult issues that commonly arise during LLM training with Model Fine-tuning.  Store the trained model safely in Model Hub.  Clear tracking and monitoring of training jobs.  In this blog, we will continue the training of meta-llama/Llama-3.2-1B with the aim of enhancing its performance in the Vietnamese language. The continual pretraining is carried out on FPT AI Studio. 1. Prepare the dataset We continue pretraining on a Vietnamese dataset to enhance the language capabilities of the LLM. The Vietnamese datasets used include:  bkai-foundation-models/BKAINewsCorpus - 5.2GB  vietgpt/wikipedia_vi - 5.6GB  Uonlp/CulturaX (Vietnamese subset) - 6GB  Ontocord/CulturaY (Vietnamese subset) - 2.4GB  10,000 Vietnamese Books - 1.7GB  This brings the total dataset size to 20.9GB, with each sample saved in .txt format. We allocate 0.1% of the data as the evaluation set, and the remaining ~99.9% as the training set (~2.8 billion tokens).  Both the training and evaluation sets are saved in .jsonl files, following FPT AI Studio's LLM training format. To use them for training, upload the .jsonl files to your S3 bucket and connect it to Data Hub.   Figure 1. Example of the .jsonl file format required by FPT AI Studio's LLM training.  To connect your S3 bucket to FPT AI Studio Data Hub:  Step 1: Go to the Data Hub tab.  Step 2: Click Create Connection.  Step 3: Fill in the S3 configuration details.  Step 4: Click Save.  Figure 2. Create Connection dialog in Data Hub. 2. Start the training We use 8 x GPU NVIDIA H100 SXM5 (128CPU - 1536GB RAM - 8xH100) and the above prepared data for continual pretraining, with the following hyperparameters:  [code lang="js"] { "batch_size": 8, "checkpoint_steps": 1000, "checkpoint_strategy": "epoch", "disable_gradient_checkpointing": false, "distributed_backend": "ddp", "dpo_label_smoothing": 0, "epochs": 2, "eval_steps": 1000, "eval_strategy": "epoch", "finetuning_type": "full", "flash_attention_v2": false, "full_determinism": false, "gradient_accumulation_steps": 16, "learning_rate": 0.00004, "logging_steps": 10, "lora_alpha": 32, "lora_dropout": 0.05, "lora_rank": 16, "lr_scheduler_type": "linear", "lr_warmup_steps": 0, "max_grad_norm": 1, "max_sequence_length": 2048, "mixed_precision": "bf16", "number_of_checkpoints": 1, "optimizer": "adamw", "pref_beta": 0.1, "pref_ftx": 0, "pref_loss": "sigmoid", "quantization_bit": "none", "save_best_checkpoint": false, "seed": 1309, "simpo_gamma": 0.5, "target_modules": "all-linear", "weight_decay": 0, "zero_stage": 1 } [/code] Setting up the Training Pipeline in FPT AI Studio:  Step 1: Create Pipeline: In FPT AI Studio, navigate to Pipeline Management and click Create Pipeline.  Figure 3. Pipeline Management interface in the Model Fine-tuning.  Step 2: Choose Template: Select the Blank template and click Let’s Start.  Figure 4. The "Choose Template" dialog within FPT AI Studio's pipeline creation process.  Step 3: Configure Base Model & Data: Fill in the information about the base model and dataset, then click Next Step.  Figure 5. "Base model & Data" of the "Create Pipeline".  Step 4: Configure Training Configuration: Select Pre-training from the Built-in Trainer dropdown, toggle Advanced, and paste the provided JSON. For Infrastructure, choose Single-node with 8 x GPU NVIDIA H100 SXM5 (128CPU - 1536GB RAM). Click Next Step.   Figure 6. "Training Configuration" of the "Create Pipeline".  Step 5: Configure Others: No test data is required due to pretraining, check the Send Email option to receive notifications upon completion, and then click Next Step.  Figure 7. "Others" of the "Create Pipeline".  Step 6: Review and Submit: Enter a name and a brief description for the pipeline run, carefully review all configured settings and click Submit.  Figure 8. "Review" of the "Create Pipeline".  Step 7: Start training:  ‘Start’ the training pipeline to begin the training process.  Figure 9. Start the training pipeline in FPT AI Studio.   During training, you can track metrics such as loss, eval loss, and learning rate in the Model Metrics tab.  Figure 10. Training metrics.  After training, the continually pretrained model is saved under Model Hub → Private Model. We can download this model for personal use.  Figure 11. The continually pretrained model in Model Hub. 3. Results After training, the loss decreased from 2.8746 to 1.9966, and the evaluation loss dropped to 2.2282, indicating that the model has effectively adapted to the Vietnamese language.  Figure 12. Loss and Eval Loss metrics during the continual pretraining process.  We evaluated the continually pretrained model on the Vietnamese benchmark ViLLM-Eval using EleutherAI EvalHarness. The results were compared against the base model. Across all tasks, the metrics showed consistent improvements - some of them substantial. For instance, on the lambada_vi task, accuracy increased from 0.2397 to 0.3478, an improvement of nearly 11%.  Model  comprehension_vi  exams_vi  lambada_vi  wikipediaqa_vi  Baseline  0.6156  0.2912  0.2397  0.321  Continued Pretraining  0.6178  0.3318  0.3478  0.397  Table 1. Performance (Accuracy) comparison of the baseline Llama-3.2-1B model and the continually pretrained model on Vietnamese benchmark tasks from VILLM-Eval.  In addition, we analyzed results on various subsets of exams_vi, covering subjects like math, physics, biology, literature and more in Vietnamese. The continually pretrained model demonstrated clear improvements over the baseline in every subject area.  Model  exams_vi _dia  exams_vi _hoa  exams_vi_su    exams_vi_sinh    exams_vi_toan    exams_vi_van    exams_vi_vatly    Baseline  0.3235  0.2522  0.2897  0.2819  0.2572  0.3192  0.2976  Continued Pretraining  0.3791  0.2609  0.3563  0.3113  0.2653  0.3662  0.3  Table 2. Detailed performance (accuracy) comparison on various subject area subsets of the exams_vi between the baseline and continually pretrained model.  These improvements demonstrate the feasibility of building high-performing Vietnamese LLMs with minimal overhead — opening the door for domain-specific applications in fintech, edtech, and more. IV. Conclusion As language and knowledge evolve at breakneck speed, Large Language Models must keep up—or risk becoming obsolete. Continual pretraining emerges as a vital solution, enabling models to seamlessly integrate new data while preserving previously learned knowledge. Unlike traditional pretraining or task-specific fine-tuning, this approach offers a scalable path to sustained performance across dynamic domains like healthcare, finance, education, and especially low-resource languages like Vietnamese.  Our experiment using FPT AI Studio demonstrated that continual pretraining is not only feasible but highly effective. By training Llama-3.2-1B on curated Vietnamese datasets, we achieved substantial performance gains across multiple benchmarks—proving that with the right tools, high-quality Vietnamese LLMs are within reach.  What sets FPT AI Studio apart is the seamless, end-to-end experience. From integrating datasets with Data Hub to orchestrating powerful GPUs and managing pipelines efficiently, FPT AI Studio removes complexity and helps your team focus on what matters most: improving your models and delivering impact faster. Whether you're developing a domain-specific chatbot, enhancing multilingual capabilities, or putting LLMs into production, FPT AI Studio provides the tools, infrastructure, and flexibility to help you build your own AI with confidence.   

1. Introduction to the GPU Container Overview of GPU Container GPU Container is a preconfigured, enterprise-grade solution designed to accelerate machine learning and data science workflows. GPU Container enables users to deploy GPU-accelerated containers that seamlessly integrate with Jupyter Notebook, providing a robust environment for developing and training machine learning models. By leveraging NVIDIA GPUs and Containerization technology, this feature delivers high-performance computing with both flexibility and scalability.  Key Benefits of GPU Container Platform High-Performance Computing: Utilize 1 to 8 NVIDIA GPUs to drastically reduce training times for complex models.  Environment Consistency: Preconfigured containers ensure reproducible setups across diverse projects, eliminating configuration conflicts.  Interactive Workflow: Jupyter Notebook provides a sophisticated interface for coding, visualization, and documentation.  Resource Optimization: Allocate GPU resources precisely, enhancing cost efficiency for scalable deployments.  Framework Versatility: Support built-in images and custom images for specialized needs.  This guide introduces  GPU Container and provides a comprehensive, step-by-step guide for setting up and using it to develop machine learning models with Jupyter Notebook.  2. Spinning up your GPU Container Prerequisites: AI Factory account with at least $5 in your account so you can experiment GPU Container without interuption in 1 or 2 hours  GPU Container is now available on FPT AI Factory, follow the steps below to spin up your GPU Container on FPT AI Factory .  Visit ai.fptcloud.com and switch to GPU Container dashboard, you can see details about your running, paused and stopped containers, all in one place.  Step 1: Select a GPU Flavor GPU Container feature allows you to choose a flavor based on your computational needs. You can select from 1 to 8 H100 NVIDIA GPUs to match your workload requirements. For example, choose 1 GPU for lightweight tasks or 8 GPUs for large-scale model training.  In the AI Factory interface, navigate to the Create New Container section.  Select the desired GPU configuration (1–8 GPUs) from the configuration panel.  Step 2: Choose a Container Image The platform offers multiple built-in images optimized for machine learning, as well as support for custom images. You can choose from preconfigured images provided within the platform. To select a built-in image, navigate to Template panel.   Alternatively, you can specify a custom Docker image if your project requires specific libraries or configurations by selecting Custom Template button and filling in URL to your custom image.  Step 3: Configure Environment Variables and Startup Commands Customize the container’s behavior by setting environment variables and startup commands, if necessary:  Environment Variables: Specify variables such as USERNAME and PASSWORD for Jupyter access or framework-specific settings.  Startup Command: Override the default command if needed (e.g., jupyter notebook --ip=0.0.0.0 --port=8888 for Jupyter). For this guide, we use the default settings for the built-in Jupyter Notebook image, which automatically starts Jupyter Notebook.  Step 4: Launch and Wait for the Container Ensuring that all configuration and pricing for your GPU Container instance meets your requirements. If everything looks good, select Create Container to spin up your container.  Wait for the container to be ready. The platform will provision the container, allocate the selected GPUs, and start the Jupyter Notebook server. You can track your container’s status on the GPU Container dashboard.   Alternatively, you can click on the name of your container on the dashboard to get more information about your set up. Once ready, the platform provides a direct link to access the Jupyter interface.   Click on the provided endpoint to gain access to Jupyter Notebook.  3. Training YOLOv11 with Jupyter Notebook in the GPU Container This section demonstrates how to upload an existing Jupyter Notebook to the GPU Container and train a YOLOv11 model for brain tumor detection.  We have prepared a notebook to demonstrate the Machine Learning/Deep Learning capability of GPU Container. Please verify the current setup.  1 H100 NVIDIA GPU is currently available in the container.  The Jupyter Notebook interface is now accessible and ready for interaction. Proceed to install the necessary packages required for model training.  After completing package installation, continue by initiating the training process.  Execute the following code snippet to start model training:  [code lang="js"] from ultralytics import YOLO  # Load a model  model = YOLO("yolo11m.pt")  # load a pretrained model (recommended for training)  # Train the model  results = model.train(data="brain-tumor.yaml", epochs=10, imgsz=640, workers=0)  [/code] Model training is now in progress using the specified parameters.  After training completes, validate the model performance by running inference on a sample input. The model completed training successfully without error. Export the trained model for deployment or future inference tasks.  Execute the following command to export the model in ONNX format:  [code lang="js"] from ultralytics import YOLO  # Load a model  model = YOLO("runs/detect/train/weights/best.pt")  # load a custom trained model   # Export the model  model.export(format="onnx")  [/code] Once the model is exported, it can be downloaded to a local environment for further use.  4. Conclusion Summary of the Workflow  This guide has demonstrated how to:  Spin up a GPU Container on FPT AI Factory by selecting a GPU configuration, choosing an image, and configuring environment variables.  Upload a Jupyter Notebook to the container and execute a machine learning model.  Persist models and notebooks for ongoing use.  GPU Container delivers a high-performance, scalable solution for machine learning, combining GPU acceleration with the flexibility of Containerization and Jupyter Notebook for enterprise-grade applications.  Recommended Next Steps  Maximize your experience with GPU Container:  Integrate with Visual Studio Code: Use the Remote - Containers extension for a unified development environment.  Serve LLM with vLLM: Enhance your workflow by integrating vLLM, a high-performance library for running large language models efficiently.  Explore Advanced Frameworks: Leverage the platform’s support for PyTorch and custom images to address complex machine learning challenges.  Deploy your model: after training and evaluation, your model can serve in a production environment. You can deploy your model with our Model Hub service.  Leverage the GPU Container feature to accelerate your machine learning workflows in enterprise environments.    

In the booming era of artificial intelligence (AI), Viet Nam is making a strong mark on the global technology map through the strategic collaboration between FPT Corporation and NVIDIA – the world’s leading provider of high-performance computing solutions, to develop FPT AI Factory, a comprehensive suite for end-to-end AI. This solution is built on the world’s most advanced AI technology, NVIDIA H100 and NVIDIA H200 superchips. Video: Mr. Truong Gia Binh (Chairman of FPT Corporation) discusses the strategic cooperation with NVIDIA in developing comprehensive AI applications for businesses According to the Government News (2024), Mr. Truong Gia Binh – Chairman of the Board and Founder of FPT Corporation – emphasized that FPT is aiming to enhance its capabilities in technology research and development, while building a comprehensive ecosystem of advanced products and services based on AI and Cloud platforms. This ecosystem encompasses everything from cutting-edge technological infrastructure and top-tier experts to deep domain knowledge in various specialized fields. "We are committed to making Vietnam a global hub for AI development." 1. Overview of the Two Superchips NVIDIA H100 & H200: A New Leap in AI Computing 1.1 Information about the NVIDIA H100 Chip (NVIDIA H100 Tensor Core GPU) The NVIDIA H100 Tensor Core GPU is a groundbreaking architecture built on the Hopper™ Architecture (NVIDIA’s next-generation GPU processor design). It is not just an ordinary graphics processing chip, but a machine specially optimized for Deep Learning and Artificial Intelligence (AI) applications. [caption id="attachment_62784" align="aligncenter" width="1200"] Chip NVIDIA H100 (GPU NVIDIA H100 Tensor Core)[/caption]   The NVIDIA H100 superchip is manufactured using TSMC's advanced N4 process and integrates up to 80 billion transistors. Its processing power comes from a maximum of 144 Streaming Multiprocessors (SMs), purpose-built to handle complex AI tasks. Notably, the NVIDIA Hopper H100 delivers optimal performance when deployed via the SXM5 socket. Thanks to the enhanced memory bandwidth provided by the SXM5 standard, the H100 offers significantly superior performance compared to implementations using conventional PCIe sockets—an especially critical advantage for enterprise applications that demand large-scale data handling and high-speed AI processing. [caption id="attachment_62802" align="aligncenter" width="1363"] NVIDIA H100 Tensor Core GPUs: 9x faster AI training and 30x faster AI inference compared to the previous generation A100 in large language models[/caption]   NVIDIA has developed two different form factor packaging versions of the H100 chip: the H100 SXM and H100 NVL, designed to meet the diverse needs of today’s enterprise market. The specific use cases for these two versions are as follows: H100 SXM version: Designed for specialized systems, supercomputers, or large-scale AI data centers aiming to fully harness the GPU’s potential with maximum NVLink scalability. This version is ideal for tasks such as training large AI models (LLMs, Transformers), AI-integrated High Performance Computing (HPC) applications, or exascale-level scientific, biomedical, and financial simulations. H100 NVL version: Optimized for standard servers, this version is easily integrated into existing infrastructure with lower cost and complexity compared to dedicated SXM systems. It is well-suited for enterprises deploying real-time AI inference, big data processing, Natural Language Processing (NLP), computer vision, or AI applications in hybrid cloud environments. Product Specifications H100 SXM H100 NVL FP64 34 teraFLOPS 30 teraFLOP FP64 Tensor Core 67 teraFLOPS 60 teraFLOP FP32 67 teraFLOPS 60 teraFLOP TF32 Tensor Core* 989 teraFLOPS 835 teraFLOP BFLOAT16 Tensor Core* 1.979 teraFLOPS 1.671 teraFLOPS FP16 Tensor Core* 1.979 teraFLOPS 1.671 teraFLOPS FP8 Tensor Core* 3.958 teraFLOPS 3.341 teraFLOPS INT8 Tensor Core* 3.958 TOPS 3.341 TOPS GPU Memory 80GB 94GB GPU Memory Bandwidth 3,35TB/s 3,9TB/s Decoders 7 NVDEC 7 JPEG 7 NVDEC 7 JPEG Max Thermal Design Power (TDP) Up to 7 MIGS @ 10GB each 350 - 400W (adjustable) Multi-Instance GPUs) Up to 7 MIGS @ 10GB each Up to 7 MIGS @ 12GB each Form Factor SXM PCIe dual-slot air-cooled Interconnect NVIDIA NVLink™: 900GB/s PCIe Gen5: 128GB/s NVIDIA NVLink: 600GB/s PCIe Gen5: 128GB/s Server Options NVIDIA HGX H100 Partner and NVIDIA- Certified Systems™ with 4 or 8 GPUs NVIDIA DGX H100 with 8 GPUs Partner and NVIDIA-Certified Systems with 1 – 8 GPUs NVIDIA AI Enterprise Optional Add-on Included Table 1.1: Specification Table of the Two H100 Chip Form Factors – H100 SXM and H100 NVL 1.2 Information about the NVIDIA H200 Chip (NVIDIA H200 Tensor Core GPU) [caption id="attachment_62803" align="aligncenter" width="1200"] Information about the NVIDIA H200 Chip (NVIDIA H200 Tensor Core GPU) including both form factors: H200 SXM and H200 NV[/caption] Building upon and advancing the Hopper™ architecture, the NVIDIA H200 Tensor Core GPU is a powerful upgrade of the H100, introduced by NVIDIA as the world’s most powerful AI chip, delivering results twice as fast as the H100 at the time of its launch in November 2023. The H200 is designed to handle even larger and more complex AI models, especially generative AI models and large language models (LLMs). Similar to the H100 superchip, NVIDIA also offers two different form factors for its H200 Tensor Core product, both designed for enterprise use: the H200 SXM and H200 NVL versions. NVIDIA H200 SXM: Designed to accelerate generative AI tasks and high-performance computing (HPC), especially with the capability to process massive amounts of data. This is the ideal choice for dedicated systems, supercomputers, and large AI data centers aiming to fully leverage the GPU’s potential with maximum NVLink scalability. Enterprises should use the H200 SXM for scenarios such as training extremely large AI models, HPC applications requiring large memory, and enterprise-level generative AI deployment. NVIDIA H200 NVL: Optimized to bring AI acceleration capabilities to standard enterprise servers, easily integrating into existing infrastructure. This version is particularly suitable for enterprises with space constraints needing air-cooled rack designs with flexible configurations, delivering acceleration for all AI and HPC workloads regardless of scale. Use cases for H200 NVL in enterprises include real-time AI inference, AI deployment in hybrid cloud environments, big data processing, and natural language processing (NLP). Product Specifications H200 SXM H200 NVL FP64 34 TFLOPS 30 TFLOPS FP64 Tensor Core 67 TFLOPS 60 TFLOPS FP32 67 TFLOPS 60 TFLOPS TF32 Tensor Core² 989 TFLOPS 835 TFLOPS BFLOAT16 Tensor Core² 1.979 TFLOPS 1.671 TFLOPS FP16 Tensor Core² 1.979 TFLOPS 1.671 TFLOPS FP8 Tensor Core² 3.958 TFLOPS 3.341 TFLOPS INT8 Tensor Core² 3.958 TFLOPS 3.341 TFLOPS GPU Memory 141GB 141GB GPU Memory Bandwidth 4,8TB/s 4,8TB/s Decoders 7 NVDEC 7 JPEG 7 NVDEC 7 JPEG Confidential Computing Supported Supported TDP Up to 700W (customizable) Up to 600W (customizable) Multi-Instance GPUs Up to 7 MIGs @18GB each Up to 7 MIGs @16.5GB each Form Factor SXM PCIe Dual-slot air-cooled Interconnect NVIDIA NVLink™: 900GB/s PCIe Gen5: 128GB/s 2- or 4-way NVIDIA NVLink bridge: 900GB/s per GPU PCIe Gen5: 128GB/s Server Options NVIDIA HGX™ H200 partner and NVIDIA-Certified Systems™ with 4 or 8 GPUs NVIDIA MGX™ H200 NVL partner and NVIDIA-Certified Systems with up to 8 GPUs NVIDIA AI Enterprise Add-on Included Table 1.2: Technical specifications of the two form factors, H200 SXM and H200 NVL 1.3 Detailed Comparison Between NVIDIA H100 and NVIDIA H200 Superchips [caption id="attachment_62804" align="aligncenter" width="1200"] The differences between the two Superchips: H100 and H200 across SXM - NVL form factors, especially in building AI infrastructure and applications for enterprises[/caption] Based on the information regarding the two NVIDIA products, H100 (H100 SXM - H100 NVL) and H200 (H200 SXM - H200 NVL), provided by FPT Cloud, here is a detailed comparison table between NVIDIA H100 & H200 for your reference: Features NVIDIA H100 (SXM) NVIDIA H100 (NVL) NVIDIA H200 (SXM) NVIDIA H200 (NVL) Architecture Hopper™ Hopper™ Inheriting and evolving from Hopper™" Inheriting and evolving from Hopper™" Manufacturing Process TSMC N4 (integrating 80 billion transistors) TSMC N4 (integrating 80 billion transistors) An upgraded version of H100 An upgraded version of H100 FP64 34 teraFLOPS 30 teraFLOP 34 TFLOPS 30 TFLOPS FP64 Tensor Core 67 teraFLOPS 60 teraFLOP 67 TFLOPS 60 TFLOPS FP32 67 teraFLOPS 60 teraFLOP 67 TFLOPS 60 TFLOPS TF32 Tensor Core 989 teraFLOPS 835 teraFLOP 989 TFLOPS 835 TFLOPS BFLOAT16 Tensor Core 1.979 teraFLOPS 1.671 teraFLOPS 1.979 TFLOPS 1.671 TFLOPS FP16 Tensor Core 1.979 teraFLOPS 1.671 teraFLOPS 1.979 TFLOPS 1.671 TFLOPS FP8 Tensor Core 3.958 teraFLOPS 3.341 teraFLOPS 3.958 TFLOPS 3.341 TFLOPS INT8 Tensor Core 3.958 TFLOPS 3.341 TFLOPS 3.958 TFLOPS 3.341 TFLOPS GPU Memory 80GB 94GB 141GB 141GB GPU Memory Bandwidth 3.35TB/s 3.9TB/s 4.8TB/s 4.8TB/s Decoders 7 NVDEC, 7 JPEG 7 NVDEC, 7 JPEG 7 NVDEC, 7 JPEG 7 NVDEC, 7 JPEG Confidential Computing No information available regarding Confidential Computing No information available regarding Confidential Computing Supported Supported Max Thermal Design Power - TDP Up to 700W (user-configurable) 350 - 400W (configurable) Up to 700W (user-configurable) Up to 600W (customizable) Multi-Instance GPUs Up to 7 Multi-Instance GPU (MIG) partitions, each with 10GB Up to 7 Multi-Instance GPU (MIG) partitions, each with 12GB Up to 7 Multi-Instance GPU (MIG) partitions, each with 18GB Up to 7 Multi-Instance GPU (MIG) partitions, each with 16.5GB Form Factor SXM PCIe interface, with a dual-slot, air-cooled design SXM PCIe interface, with a dual-slot, air-cooled design Interconnect NVIDIA NVLink™: 900GB/s;; PCIe Gen5: 128GB/s NVIDIA NVLink: 600GB/s;; PCIe Gen5: 128GB/s NVIDIA NVLink™: 900GB/s; PCIe Gen5: 128GB/s NVIDIA NVLink 2- or 4-way bridge: 900GB/s per GPU; PCIe Gen5: 128GB/s Server Options NVIDIA HGX H100 Partner and NVIDIA-Certified Systems™ with 4 or 8 GPUs; NVIDIA DGX H100 with 8 GPUs Compatible with Partner and NVIDIA-Certified Systems supporting 1 to 8 GPUs Supported on NVIDIA HGX™ H200 Partner Systems and NVIDIA-Certified Platforms featuring 4 or 8 GPUs NVIDIA MGX™ H200 NVL Partner & NVIDIA-Certified Systems (up to 8 GPUs) NVIDIA AI Enterprise Add-on Included Add-on Included Table 1.2: Detailed comparison table between NVIDIA H100 (SXM - NVL) and NVIDIA H200 (SXM - NVL) 2. FPT strategically partners with NVIDIA to develop the first AI Factory in Vietnam The strategic synergy between NVIDIA, a leading technology company, and FPT's extensive experience in deploying enterprise solutions has forged a powerful alliance in developing pioneering AI products for the Vietnamese market. NVIDIA not only supplies its cutting-edge NVIDIA H100 and H200 GPU superchips but also shares profound expertise in AI architecture. For FPT Corporation, FPT Smart Cloud will be the trailblazing entity to provide cloud computing and AI services built upon the foundation of this AI factory, enabling Vietnamese enterprises, businesses, and startups to easily access and leverage the immense power of AI. [caption id="attachment_62805" align="aligncenter" width="2560"] FPT Corporation is a strategic partner of NVIDIA in building and developing the FPT AI Factory solutions: FPT AI Infrastructure, FPT AI Studio, and FPT AI Inference[/caption]   Notably, FPT will concentrate on developing Generative AI Models, offering capabilities for content creation, process automation, and solving complex problems that were previously challenging to address. In the era of burgeoning AI technologies, B2B enterprises across all sectors—from Finance, Securities, and Insurance to Manufacturing and Education are facing a pressing need for a reliable partner to achieve digital transformation breakthroughs. FPT AI Factory from FPT Cloud is the optimal solution, offering your business the following outstanding advantages: Leading AI Infrastructure: By directly utilizing the NVIDIA H100 and H200 superchips, FPT AI Factory delivers a powerful AI computing platform, ensuring superior performance and speed for all AI tasks. Diverse Service Ecosystem: FPT AI Factory is not just hardware but a comprehensive ecosystem designed to support businesses throughout the entire AI solution lifecycle—from development and training to deployment. Cost Optimization: Instead of investing millions of dollars in complex AI infrastructure, businesses can leverage FPT AI Factory as a cloud service, optimizing both initial investment and operational costs. Security, Compliance, and Integration: FPT is committed to providing a secure AI environment that meets international security standards while also enabling seamless integration with existing enterprise systems. [caption id="attachment_62806" align="aligncenter" width="1642"] The superior advantages of the FPT AI Factory solution for businesses across various industries in the market[/caption] 3. Building a Comprehensive FPT AI Factory Ecosystem (FPT AI Infrastructure, FPT AI Studio, and FPT AI Inference) Powered by NVIDIA H100 & H200 Superchips FPT AI Factory currently offers a trio of AI solutions developed based on the core technology of NVIDIA H100 & NVIDIA H200 superchips for enterprises, including: FPT AI Infrastructure: This is the group of products related to enterprise infrastructure. FPT AI Studio: This is the group of products related to the platform of tools and services for enterprises. FPT AI Inference: This is the group of products related to the platform for AI (Artificial Intelligence) and ML (Machine Learning) models for enterprises. Video: FPT’s trio of AI solutions — FPT AI Infrastructure, FPT AI Studio, and FPT AI Inference — enables businesses to build, train, and operate AI solutions simply, easily, and effectively. 3.1 FPT AI Infrastructure Solution [caption id="attachment_62807" align="aligncenter" width="1528"] The FPT AI Infrastructure solution enables businesses to deploy high-performance computing infrastructure, develop AI solutions, and easily scale according to demand[/caption] FPT AI Infrastructure is a robust cloud computing infrastructure platform, specially optimized for AI workloads. It provides superior computing power from NVIDIA H100 and H200 GPUs, enabling enterprises to build supercomputing infrastructure, easily access and utilize resources to train AI models rapidly, and flexibly scale according to their needs using technologies such as Meta Cloud, GPU Virtual Machine, Managed CPU Cluster, and GPU Container. Register for FPT AI Infrastructure today to build and develop powerful infrastructure for your business! 3.2 The FPT AI Studio Product [caption id="attachment_62808" align="aligncenter" width="1587"] The FPT AI Studio product helps businesses process data, develop, train, evaluate, and deploy artificial intelligence and machine learning models based on their specific needs[/caption] Once a business has established an infrastructure system with advanced GPU technology, the next step is to build and develop its own artificial intelligence and machine learning models tailored to specific operational and application needs. FPT AI Studio is the optimal solution for this. It is a comprehensive AI development environment that offers a full suite of tools and services to support businesses throughout the entire process from data processing, model development, training, evaluation, to deployment of real-world AI/ML models—using cutting-edge technologies such as Data Hub, AI Notebook, Model Pre-training, Model Fine-tuning, and Model Hub. Register now to start building and deploying AI and Machine Learning models for your business today! 3.3 The FPT AI Inference Service [caption id="attachment_62809" align="aligncenter" width="1580"] FPT AI Inference service enhances the inference capabilities for enterprises' AI and Machine Learning models[/caption] Once an enterprise's AI or Machine Learning model has been trained using internal and other crucial data, deploying and operating it in a real-world environment demands an efficient solution. FPT AI Inference is the intelligent choice for your business. This solution is optimized to deliver high inference speed and low latency, ensuring your AI models can operate quickly and accurately in real-world applications such as virtual assistants, customer consultation services, recommendation systems, image recognition, or natural language processing, powered by advanced technologies like Model Serving and Model-as-a-Service. This is the final piece in the FPT AI Factory solution suite, helping enterprises to put AI into practical application and deliver immediate business value. Enhance the inference capabilities and real-world applications of your enterprise AI models today with FPT AI Inference! 4. Exclusive offer for customers registering to experience FPT AI Factory on FPT Cloud [caption id="attachment_62810" align="aligncenter" width="1312"] Special benefits for businesses when registering to use FPT AI Factory services as early as possible[/caption] Exclusive incentives from FPT Cloud just for you when you register early to experience the comprehensive AI Factory solution trio: FPT AI Infrastructure, FPT AI Studio, and FPT AI Inference today:  Priority access to FPT AI Infrastructure services at preferential pricing: Significantly reduce costs while accessing world-class AI infrastructure, tools, and applications—right here in Vietnam. Early access to premium features of FPT AI Factory: Ensure your business stays ahead by being among the first to adopt the latest AI technologies and tools in the digital transformation era. Receive Cloud credits to explore a diverse AI & Cloud ecosystem: Experience other powerful FPT Cloud solutions that enhance operational efficiency, such as FPT Backup Services, FPT Disaster Recovery, and FPT Object Storage. Gain expert consultation from seasoned AI & Cloud professionals: FPT’s AI and Cloud specialists will support your business in applying and operating the FPT AI Factory solution suite effectively, driving immediate business impact. Register now to receive in-depth consultation on the FPT AI Factory solution from FPT Cloud’s team of experienced AI & Cloud experts! [caption id="attachment_62811" align="aligncenter" width="963"] Registration Form for Expert AI & Cloud Consultation on FPT AI Factory's Triple Solution Suite for Enterprises[/caption]

LandingAI, a Silicon Valley-based leader in agentic vision technologies founded by Dr. Andrew Ng, is leveraging FPT AI Factory services to accelerate the development of its tools, including Agentic Document Extraction, Agentic Object Detection, and VisionAgent. Through this partnership, LandingAI utilizes Metal Cloud, powered by NVIDIA H100 Tensor Core GPUs, to meet the growing demand for high-performance computing, scalability, and operational efficiency. LandingAI is redefining visual intelligence with its tools, applying an agentic AI framework designed to help users solve complex visual tasks using unstructured data such as images and documents. The system intelligently selects and orchestrates vision models and generates deployable code to automate similar tasks in the future. A key challenge in developing the Visual AI platform lies in the need for substantial computing resources to fine-tune the agents, run reinforcement learning loops, and drive continuous performance improvement while ensuring rapid iteration speed to keep pace with innovation. Tackling Computational Challenges with Metal Cloud FPT AI Factory offers the critical infrastructure needed to fast-track the development of the Visual AI platform and address performance complexities. Through the partnership with FPT, LandingAI gains access to Metal Cloud - a high-performance AI infrastructure fueled by NVIDIA H100 GPUs, backed by high SLAs and continuous support by FPT’s experts.  The cutting-edge GPUs deliver the computational power necessary for supervised fine-tuning and reinforcement learning at scale, thus enabling rapid and efficient model development. The seamless integration and minimal setup friction further allow LandingAI to quickly incorporate the H100s into its training pipeline and iterate on model architectures and agent behaviors at unprecedented speed and efficiency.  In addition, LandingAI is able to expand its computing capacity while optimizing resource consumption with the competitive pricing of FPT AI Factory services. Key benefits achieved: Significant improvements in visual task generalization  3X faster deployment of customer-facing features “As LandingAI expands our agentic vision technology offerings, FPT AI Factory has provided us with a solid and flexible infrastructure for our large-scale AI development and deployment,” said Mr. Dan Maloney, CEO of LandingAI. “Their system's reliability and flexibility have streamlined our Visual AI workflows, significantly reducing iteration time. We have seen improved operational stability in production and cost savings. Their responsive support has made integration seamless.” Agentic Document Extraction Playground A Solid Foundation for Agentic AI Innovation FPT AI Factory is a full-stack ecosystem for end-to-end AI development, designed to make AI accessible, scalable, and tailored to each business’s unique goals. Powered by thousands of NVIDIA Hopper H100/H200 GPUs, combined with the latest NVIDIA AI Enterprise software platform, FPT AI Factory provides robust infrastructure, foundational models, and necessary tools for businesses to build and advance AI applications from the ground up with faster time-to-market and enterprise-grade performance at a fraction of traditional costs.  As the global demand for Agentic AI systems gains momentum to transform business task automation with minimal effort, LandingAI’s integration of FPT AI Factory demonstrates the potential of high-performance, flexible AI infrastructure to drive innovation in this fast-growing domain. These agentic systems, designed to perform complex tasks using natural language prompts, are not only reshaping automation and collaboration but also making advanced AI capabilities more approachable for developers, engineers, and business users alike.  The AI Agent market value is projected to reach $52.62 billion by 2030 with a CAGR of 46.3% from 2025 to 2030. Built on a low-code or no-code platform, AI Agents are fostering faster AI adoption and more dynamic human-AI collaboration across various sectors. The computing power and agility provided by FPT AI Factory emerge as critical enablers for businesses to enter and lead in the next era of intelligent automation.  “FPT and LandingAI share a mutual vision to democratize AI and make its powerful capabilities accessible to all. This collaboration marks another milestone in our long-term partnership to establish a strong foundation for developing next-generation AI technologies, such as Agentic AI, driving innovation and bringing tangible value across multiple industries,” shared Mr. Le Hong Viet, CEO of FPT Smart Cloud, FPT Corporation. Looking ahead, FPT is committed to continuously enhancing the FPT AI Factory to further eliminate infrastructure barriers and simplify AI development, empowering businesses to innovate faster, smarter, and more efficiently. About FPT Corporation FPT Corporation (FPT) is a global leading technology and IT services provider headquartered in Vietnam. FPT operates in three core sectors: Technology, Telecommunications, and Education. As AI is indeed a key focus, FPT has been integrating AI across its products and solutions to drive innovation and enhance user experiences within its Made by FPT ecosystem. FPT is actively working on expanding its capabilities in AI through investments in human resources, R&D, and partnerships with leading organizations like NVIDIA, Mila, AITOMATIC, and LandingAI. These efforts are aligned with FPT's ambitious goal to solidify its status among the world's top billion-dollar IT companies. For more information, please visit https://fpt.com/en. About LandingAI LandingAI™ delivers cutting-edge agentic vision technologies that empower customers to unlock the value of visual data. With LandingAI’s solutions, companies realize the value of AI and move AI projects from proof-of-concept to production.  Guided by a data-centric AI approach, LandingAI’s flagship product, LandingLens™, enables users to build, iterate, and deploy Visual AI solutions quickly and easily. LandingAI is a pioneer in agentic vision technologies, including Agentic Document Extraction and Agentic Object Detection, which enhance the ability to process and understand visual data at scale, making sophisticated Visual AI tools more accessible and efficient.  Founded by Andrew Ng, co-founder of Coursera, founding lead of Google Brain, and former chief scientist at Baidu, LandingAI is uniquely positioned to lead the development of Visual AI that benefits all. For more information, visit https://landing.ai/.

Global leading IT firm FPT has announced a partner ecosystem of global pioneer technology organizations, including NVIDIA, SCSK, ASUS, Hewlett Packard Enterprise, VAST Data, and DDN Storage. This cooperative endeavor is to expedite AI factory development and operations in Vietnam and Japan. Dr. Truong Gia Binh and senior leaders of FPT, along with representatives of NVIDIA, SCSK, ASUS, Hewlett Packard Enterprise, VAST Data, and DDN Storage, announced a partner ecosystem to promote the AI Factory in Vietnam and Japan. This partner ecosystem commits to combining expertise, resources, and networks to unlock FPT AI Factory’s potential as a powerhouse for ever-growing AI innovation while reinforcing sovereign AI in Vietnam and Japan. To this end, the collaboration focuses on four key objectives: 1) Promoting the development and operations of AI Factories in Vietnam and Japan, following global standards; 2) Diversifying a portfolio of AI products and services; 3) Enriching human technical capabilities; and 4) Guarding data security and autonomy. FPT also revealed the launch of FPT AI Factory in Vietnam and Japan, enabling businesses of all sizes to expedite AI innovation with priority access to premium solutions and features through an exclusive pre-order. FPT AI Factory offers an all-inclusive stack for end-to-end AI development that leverages thousands of NVIDIA H200 and H100 Tensor Core GPUs with the NVIDIA AI Enterprise software platform, which includes NVIDIA NeMo. FPT AI Factory grants organizations, researchers, and innovators scalable GPU supercomputing to cultivate sophisticated AI solutions with faster time-to-market while safeguarding sensitive information and maintaining sovereignty. This suite also authorizes clientele to manage resources and processes expeditiously for large-scale AI and machine learning workloads, achieving up to 45% better total cost of ownership. Mr. Le Hong Viet - CEO, FPT Smart Cloud, FPT Corporation unveiled the future of digital autonomy empowered by FPT AI Factory This flagship stack consists of four main product groups: FPT AI Infrastructure offers enterprise accelerated computing cloud services with the latest technology, top performance, flexibility, and scalability to accelerate model development. Enterprises can enjoy first-rate infrastructure performance at a massive scale for most compute-intensive AI tasks. A unified management system with built-in security allows complete control over the AI computing environment and data throughout the development process. FPT AI Studio is a trusted and inclusive platform that streamlines the AI creation process in a fast and safe manner. It provides a comprehensive set of smart tools to effortlessly explore, develop, evaluate, and deploy custom models enriched and differentiated with corporations’ large-scale data. That benefits businesses in creating cutting-edge AI applications from scratch without requiring deep expertise, securely simplifying operations, and improving AI efficiency. FPT AI Inference is a robust platform that augments AI capabilities with a broad collection of high-performing models for immediate use. Businesses can leverage numerous foundational and FPT-developed models to rapidly fine-tune and deploy models tailored to industry requirements. They can also scale these models in terms of size and number of usages for unique applications hosted on NVIDIA-Certified Systems. FPT AI Agents is a state-of-the-art platform to create and operate multi-lingual AI Agents fueled by the business knowledge base and custom models for specific tasks in customer service, corporate training, internal operations, etc. Developed on the powerful FPT AI Factory infrastructure with advanced generative AI cores, FPT AI Agents will enable businesses to unlock unprecedented productivity, take service quality to new heights, transform the workforce, and achieve borderless innovation. FPT AI Factory is integrated with 20+ ready-to-use AI products, built upon Generative AI, for rapid AI adoption and instant results in elevating customer experience, achieving operational excellence, transforming the human workforce, and optimizing operating expenses. FPT is now accepting exclusive advance orders for FPT AI Factory, letting corporate clients utilize the diverse product and service offerings of AI and cloud, earn cloud credit, and gain early access to premium features. Combined with customized consultation from seasoned AI and Cloud experts, enterprises in any industry can reinforce successful AI journeys with practical, high-value solutions. Since its $200-million investment plan announced in April 2024, FPT has been working closely with NVIDIA to mobilize resources and dedicate to creating groundbreaking products, aiming to ignite profound changes, including customer service and workforce development. With the launch of FPT AI Factory, FPT is completing an end-to-end ecosystem comprising superior infrastructure, intelligent platforms, beneficial applications, and professional services, all designed to meet the dynamic demands of AI evolution. Dr. Truong Gia Binh - Founder of FPT Corporation reclaimed the shared vision of co-creating the future with AI Dr. Truong Gia Binh, Chairman and Founder of FPT Corporation: “AI factories are emerging as an essential foundation for the human and AI agent economy, representing a new transformative force in the digital landscape. Through the collaborative efforts to establish the omnipotent FPT AI Factory, we empower organizations, researchers, developers, and adopters to seize the full potential of AI, forming thousands of intelligent agents enriched and differentiated by the data, knowledge, and culture of every business and nation. Step by step, we redefine and perfect production relations between AI and humans, enhancing the competitiveness of every economy while promoting international integration.” Mr. Dennis Ang, Senior Director, Enterprise Business of ASEAN and ANZ Region, NVIDIA: “AI is reshaping nations and industries. Leveraging the full-stack NVIDIA AI platform, FPT is set to more efficiently scale its AI factory to support enterprises throughout the region.” Mr. Dennis Ang - Senior Director of NVIDIA emphasized the role of AI Factory in shaping the new era of technology Mr. Masaki Komine, Managing Executive Officer, General Manager, Products & Services Business Group, SCSK Corporation: “We believe that we share the goal of fundamentally changing the digital environment with FPT. We value the spirit of co-creation and hope to build a long-term cooperative relationship with a passionate partner like FPT. Furthermore, we would like to utilize our cutting-edge AI infrastructure technology and knowledge gained from many years of system operation to tackle the challenges faced by customers and societies around the world, including Japan and Vietnam.” Mr. Jason Chung, Regional Director of East Asia and Indochina, ASUS: ‘’ASUS is one of the main partners of the NVIDIA Cloud Partner Program. With our experience in large-scale AI server deployment and operations, ASUS is excited to partner with FPT and NVIDIA in this pioneering venture. Together, we're empowering businesses in Vietnam and Japan to harness the power of AI. By fostering innovation and collaboration, we’re building an ecosystem that will ensure that no business is left behind in the AI era.” Mr. Narinder Kapoor, Senior Vice President and Managing Director of APAC, Hewlett Packard Enterprise: “The era of AI has brought with it limitless possibility. HPE is committed to staying at the forefront of innovation to help customers embrace this new era. This launch marks a significant milestone in our collaboration with FPT and NVIDIA to enable enterprises in Vietnam, Japan and across Asia Pacific to harness the full potential of AI. We expect this initiative to accelerate AI adoption and bolster data sovereignty and security, which are critical for our customers in this digital age. We are excited to support this transformative journey and look forward to being part of this initiative supporting Vietnam as it transforms into a hub of AI innovation.” Mr. Sunil Chavan, Vice President of Asia-Pacific, VAST Data: “We’re thrilled to support FPT AI Factory’s launch, which will help redefine how enterprises in Vietnam and Japan harness the power of AI. Today's enterprises are looking at how AI can help them extract real business value. To realize these benefits, they need scalable, reliable solutions that deliver clear returns on their capital investments. With VAST Data’s robust infrastructure, we’re helping companies build flexible, compliant AI ecosystems that integrate seamlessly with existing environments and support a wide range of AI initiatives. This partnership allows businesses to make strategic, data-driven decisions with the confidence that they have a solution tailored to their complex needs.” Mr. Robert Triendl, SVP International and General Manager, DDN Storage: “We congratulate FPT on the launch of “FPT AI Factory”, and we are very excited to have been selected as the performance data solution for this innovative GPU cloud service. DDN’s AI solutions combine superior performance with minimal data center footprint to support the most scalable AI workloads today. We look forward to collaborating closely with FPT to build powerful new services for next-generation AI workloads, and we wish FPT great success in Vietnam, Japan, and the global market.” FPT Corporation (FPT) is a global leading technology and IT services provider headquartered in Vietnam. FPT operates in three core sectors: Technology, Telecommunications, and Education. As AI is indeed a key focus, FPT has been integrating AI across its products and solutions to drive innovation and enhance user experiences within its Made by FPT ecosystem. FPT is actively working on expanding its capabilities in AI through investments in human resources, R&D, and partnerships with leading organizations like NVIDIA, Mila, AITOMATIC, and Landing AI. These efforts are aligned with FPT's ambitious goal to solidify its status among the world's top billion-dollar IT companies. For more information, please visit https://fpt.com/en.

Jupyter Notebook is a browser-based interface that allows users to interact directly with code and data through a user-friendly web UI. It is commonly used in AI tasks such as data exploration, feature extraction, model building, and experimentation.  This guide provides a quick walkthrough for deploying GPU Notebooks on FPT AI Factory—from infrastructure setup to accessing and running AI notebooks for tasks like data analysis, feature engineering, model training, and inference.  I. Service Requirements To deploy a GPU Notebook on FPT AI Factory, users need to:  Register an account at https://id.fptcloud.com   Contact the sales team to subscribe to the FPT AI Factory – AI Infrastructure service.  Once registered, the technical team will provision the necessary resources for service access.  II. Setting Up and Accessing the GPU Notebook The environment setup involves two virtual machines within the same VPC:  Jump Server: acts as an SSH gateway for external access.  GPU VM: the main virtual machine for running the notebook and handling AI workloads.  Step 1: Create GPU VM  Create a GPU VM with H100 configuration using the recommended template (16 CPUs, 192 GB RAM, 80 GB GPU RAM). Reference: https://fptcloud.com/en/documents/gpu-virtual-machine-en/?doc=quick-start  Network configuration: assign a public IP, open notebook ports, and configure access permissions via Security Group.  Step 2: Environment Setup  Update the system and install the GPU driver:  [code lang="js"] sudo apt update && sudo apt upgrade -y  sudo apt install -y nvidia-driver-565  nvidia-smi  # kiểm tra trạng thái GPU  [/code] Install Docker following the official guide: https://docs.docker.com/engine/install/ubuntu/  Install NVIDIA Container Toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html  Step 3: Launch Jupyter Notebook Container  [code lang="js"] image="quay.io/jupyter/tensorflow-notebook:cuda-python-3.11"  docker run -p 8888:8888 \    -v ~/work:/home/jovyan/work \    --detach \    --name notebook \    --gpus all \  $image  [/code] Step 4: Retrieve Access Token  [code lang="js"] docker ps            # lấy container ID  docker logs -f <ID>  # tìm token trong log  [/code] Step 5: Access Notebook via SSH Tunnel  Open a browser and go to http://localhost:13888 using the token retrieved in Step 4.  [code lang="js"] ssh -L 13888:127.0.0.1:8888 -J <user_jump>@<jump_ip> <user_vm>@<vm_ip>  [/code] III. Running Basic Notebooks  After successfully accessing Jupyter Notebook, users can run notebooks to validate the setup:  1. Check GPU with TensorFlow [code lang="js"] import tensorflow as tf  tf.config.list_physical_devices()  [/code] 2. Directly test GPU driver  Run mnist-example notebook 3. Try Stable Diffusion (Optional) https://github.com/nebuly-ai/learning-hub/blob/main/notebooks/notebooks/stable-diffusion.ipynb  Conclusion  This guide outlines a step-by-step process for deploying a GPU Notebook environment on FPT Smart Cloud’s AI Factory infrastructure. It enables users to easily spin up virtual machines, configure the environment, and run basic AI models such as TensorFlow or GPU-based inference.  The deployment model using a Jump Server ensures secure external access while offering flexibility for scaling and experimenting with more advanced AI workloads. This platform is ideal for research teams, product development, or enterprises aiming to rapidly prototype and test AI models without upfront hardware investment. 

Hanoi, April 22, 2025 – FPT Corporation announced a strategic partnership with Sumitomo Corporation and SBI Holdings - Japan’s leading conglomerates in the finance and industrial sectors to accelerate artificial intelligence (AI) adoption through the FPT AI Factory ecosystem, contributing to the advancement of sovereign AI in Japan. Under this partnership, Sumitomo Corporation and SBI Holdings will each invest 20% and 20% in FPT Smart Cloud Japan, a subsidiary of FPT Corporation. This partnership lays a critical foundation for delivering cutting-edge AI solutions to organizations and enterprises in Japan, expediting AI integration across all aspects of society and supporting the nation’s ambition to become a global AI leader. Combining FPT’s technological capabilities with the extensive networks and expertise of Sumitomo Corporation and SBI Holdings across various industries, the three parties are committed to scaling AI and Cloud business in Japan. Together, they aim to build a diversified product and service ecosystem that meets the unique and increasingly complex demands of the Japanese market. FPT’s Chairman Mr. Truong Gia Binh, SBI Holdings Representative Director, Chairman, President & CEO Mr. Yoshitaka Kitao, and Sumitomo Corporation Director, Vice Chairman Mr. Toshikazu NAMBU signed an investment joint venture in Japan. Mr. Truong Gia Binh, Founder and Chairman of FPT Corporation, emphasized: “Sharing a common vision for the transformative potential of AI, we are working closely with our strategic partners to expand the global application of AI technologies. This partnership also contributes to fostering innovation, strengthening organizational competitiveness, and maintaining technology autonomy, supporting Japan’s goal of becoming an AI nation.” With the core philosophy of “Build Your Own AI,” FPT AI Factory aims to make AI more accessible and easily deployable for every business, organization, and individual. Leveraging FPT’s robust AI infrastructure - powered by thousands of the latest-generation GPUs, pre-packaged models, and deployment frameworks, alongside a comprehensive service ecosystem as well as proven experience in the Japanese market of FPT and its investors, FPT AI Factory enables organizations to harness their proprietary data, knowledge, and identity. This empowers them to rapidly develop tailored AI applications, unlock breakthrough performance, and create sustainable competitive advantages. About SBI Group Established in 1999, SBI Group is one of Japan’s pioneers in online financial services. The Group operates a wide range of financial businesses, offering user-friendly products and services via the Internet, primarily in the areas of securities, banking, and insurance. In addition to its core operations, SBI is also active in asset management and various global investment ventures. About Sumitomo Corporation Sumitomo Corporation (TYO: 8053) is an integrated trading and business investment company with a strong global network comprising 125 offices in 64 countries and regions. The Sumitomo Corporation Group consists of approximately 900 companies and 80,000 employees on a consolidated basis. The Group's business activities are spread across the following nine groups: Steel, Automotive, Transportation & Construction Systems, Diverse Urban Development, Media & Digital, Lifestyle Business, Mineral Resources, Chemicals Solutions and Energy Transformation Business. Sumitomo Corporation is committed to creating greater value for society under the corporate message of "Enriching lives and the world," based on Sumitomo’s business philosophy passed down for over 400 years. About FPT Corporation  FPT Corporation (FPT) is a global leading technology and IT services provider headquartered in Vietnam. FPT operates in three core sectors: Technology, Telecommunications, and Education. As AI is indeed a key focus, FPT has been integrating AI across its products and solutions to drive innovation and enhance user experiences within its Made by FPT ecosystem. FPT is actively working on expanding its capabilities in AI through investments in human resources, R&D, and partnerships with leading organizations like NVIDIA, Mila, AITOMATIC, and Landing AI. These efforts are aligned with FPT's ambitious goal to reach 5 billion USD in IT services revenue from global markets by 2030 and solidify its status among the world's top billion-dollar IT companies.  After nearly two decades in Japan, FPT has become one of the largest foreign-invested technology firms in the country by human resource capacity. The company delivers services and solutions to over 450 clients globally, with over 4,000 employees across 17 local offices and innovation hubs in Japan, and nearly 15,000 professionals supporting this market worldwide.   With Japan as a strategic focus for the company’s global growth, FPT has been actively expanding its business and engaging in M&A deals, such as the joint venture with Konica Minolta, strategic investment in LTS Inc, and most recently, the acquisition of NAC—its first M&A deal in the market. As digital transformation, particularly legacy system modernization, is viewed as a key growth driver in the Japanese market, the company is committed to providing end-to-end solutions and seamless services, utilizing advanced AI technologies as a primary accelerator. For more information, please visit https://fpt.com/en.