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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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1. What is Model Fine-tuning?
Model Fine-tuning is the process of retraining a base language model on a specialized dataset so that it performs better in a specific domain or for a targeted use case.
2. Which model should I choose for fine-tuning?
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Small models (<=1B parameters) --> for testing or light workloads
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Medium models (7B-13B) --> balanced performance and cost
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Large models (30B+) --> for complex tasks, usually requires multi-node setup
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Instruction-tuned models are preferred if your task is prompt-response based
3. How long does fine-tuning take?
It depends on:
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Model size (a few hours for small models, several days for very large ones)
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Dataset size
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Your hardware setup (hyperparameters & infrastructure)
Typically, it ranges from a few hours to several days.
4. 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.).
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.
7. What is the minimum GPU memory required?
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At least 24GB VRAM per GPU for standard fine-tuning
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Without LoRA/QLoRA methods, you can fine-tune on GPUs with 8-16GB VRAM
8. Does the size of my training dataset affect hardware needs?
Yes. Larger datasets require more VRAM, RAM, and storage.
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Datasets < 20GB --> can use Managed volume
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Datasets > 20GB --> require Dedicated network volume