- About Us
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Secure, unlimited storage to ensures efficiency as well as high and continuous data access demand.
Virtual server integration for 3D Rendering, AI or ML
Enhance application capacity and availability.
Access to an all-inclusive stack for AI development, driven by NVIDIA’s powerful technology!
FPT Web Application Firewall provides powerful protection for web applications
Advanced virtual server with rapid scalability
Backup and restore data instantly, securely and maintain data integrity.
Advanced virtual server with rapid scalability
Access to an all-inclusive stack for AI development, driven by NVIDIA’s powerful technology!
Enhance application capacity and availability.
Backup and restore data instantly, securely and maintain data integrity.
Recovery, ensuring quick operation for the business after all incidents and disasters.
Diverse throughput and capacity to meet various business workloads.
Secure, unlimited storage to ensures efficiency as well as high and continuous data access demand.
FPT Web Application Firewall provides powerful protection for web applications
Intelligent and Comprehensive Virtual Web Application Firewall - Security Collaboration between FPT Cloud and Penta Security.
The Next generation firewall security service
Easily store, manage, deploy, and secure Container images
Safe, secure, stable, high-performance Kubernetes platform
Provided as a service to deploy, monitor, backup, restore, and scale MongoDB databases on cloud.
Provided as a service to deploy, monitor, backup, restore, and scale Redis databases on cloud.
Provided as a service to deploy, monitor, backup, restore, and scale PostgreSQL databases on cloud.
System Monitoring Solution anywhere, anytime, anyplatform
Helps reduce operational costs by up to 40% compared to traditional BI solutions, while improving efficiency through optimized resource usage and infrastructure scaling. - Pricing
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Model Fine-Tuning
1. What is Model Fine-tuning?
Fine-tuning is the process of further a pre-trained AI model using your own data so that it better understands your specific context, style, or objectives.
2. What do your need to prepare before fine-tuning a model?
You'll need:
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Strictly follow the expected dataset structure for the model you're fine-tuning. More details about Data sample, visit here: https://fptcloud.com/en/documents/model-fine-tuning/?doc=select-dataset-format
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Clean, diverse, and non-duplicated data.
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A clear objective for fine-tuning (e.g., tech support, customer service, content writing, etc.).
3. Which model should I choose for fine-tuning?
Depends on your needs:
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Under 1B parameters: fast, cost-efficient, good for lightweight devices.
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7B - 13B: balanced between quality and performance.
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Over 30B: ideal for demanding, high-quality applications.
4. How long does fine-tuning take?
It depends on:
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Model size
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Amount of training data.
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Your hardware setup.
Typically, it ranges from a few hours to several days.
5. How many GPUs do you need to fine-tune a model?
It depends on the model size:
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<1B parameters: 1 GPU (24 GB VRAM) is sufficient
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7B models: 2-4 GPUs (40 GB VRAM each)
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13B models: 4-8 GPUs recommended
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30B+ models: Requires 8+ GPUs and multi-node setup
6. Do I need multiple nodes or just one node?
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For small to medium models (up to 13B), a single node with multiple GPUs is enough.
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For larger models (30B+), multi-node setups are recommended for better memory and performance.