Model fine-tuning
Harness the power of Nebius AI cloud infrastructure to refine your machine learning models. By leveraging the latest GPUs, you can dramatically reduce training times and achieve the highest levels of precision.
GPUs for different workloads
We offer AI-tailored NVIDIA A100 and H100 GPUs in DELTA HGX Baseboards with 8 GPUs connected by NVLink, as well as L40s and other GPUs in the PCIe form factor.
Support and onboarding assistance
We provide onboarding, assistance with complex cases and with optimizing platform usage, reducing your problem-solving time.
Marketplace
Leading vendors' AI-specific tools including OS images and Kubernetes® apps will make a perfect workplace for data scientists and ML engineers.
How to choose GPU for fine-tuning
V100
V100 with NVLink is a good choice for fine-tuning of diffusion and transformer models, e.g. Llama2 and Stable Diffusion, as well as non-transofrmer models like Resnet-50 and Wav2Vec2.
А100
Cost effective for fine-tuning of conventional models.
Great for domains where CNN, RNN models are popular, e. g. computer vision or medical diagnostics.
Н100
Best choice if speed is your top priority.
Perfect for bigNLP, LLM, and all models with Transformer architecture.
H200
The world’s most powerful GPU for supercharging AI and HPC workloads coming soon!
Solution architecture
Solution architecture
This set of Nebius AI services will enable you to create an environment and a data pipeline for self-supervised, supervised or reinforcement learning.
Let’s find the best possible technical solution
If you want to use a specific database or third-party software for your project, our team of solution architects is here to assist you at every step of the way.
FAQ and basic terminology
What is fine-tuning?
What is fine-tuning?
Fine-tuning is a process in machine learning where a pre-trained model, often a deep learning model, is further trained on a specific task or dataset to improve its performance for that task. Instead of training a model from scratch, fine-tuning leverages the knowledge and features learned from a broader dataset, making it a more efficient approach for specific tasks.
What precautions should be taken during fine-tuning?
What precautions should be taken during fine-tuning?
Can fine-tuning overcome data scarcity issues?
Can fine-tuning overcome data scarcity issues?
Can fine-tuned models be used in real-world applications?
Can fine-tuned models be used in real-world applications?