Turn pretrained models into production-ready AI apps with GPU fine-tuning on DigitalOcean
Fine-tune large language models (LLMs), vision models, or multimodal systems with flexible GPU options—whether you’re adapting open-source models to domain-specific tasks or refining enterprise AI solutions. Our cloud GPUs let you iterate quickly, reduce training costs, and avoid the complexity of managing on-premise infrastructure.
Simplify AI model customization with DigitalOcean GPUs
Pre-trained models give you a head start, but real AI impact comes from tailoring those models to your own data. Whether you’re fine-tuning LLMs, vision models, or multimodal systems, customizing AI for specific tasks requires powerful GPU infrastructure. DigitalOcean’s GPU offerings simplify this process. You get the flexibility to run full fine-tuning, few-shot adaptation, or quantization-aware retraining on DigitalOcean’s Gradient GPU Droplets, without the overhead of managing hardware, servers, or GPU drivers. Focus on your model optimization, and launch your AI workloads in minutes with scalable, developer-friendly GPU compute.
Fine-tune AI models with precision on DigitalOcean GPUs
Whether you're customizing a large language model for domain-specific tasks or refining a vision model for unique datasets, DigitalOcean provides the GPU infrastructure and tooling to accelerate fine-tuning workflows. Scale your workloads with multi-GPU setups when needed, and deploy your custom models directly into production environments on the same platform.
Choose your fine-tuning strategy
Run your preferred fine-tuning techniques, including full parameter updates, LoRA (Low-Rank Adaptation), QLoRA, PEFT (Parameter-Efficient Fine-Tuning), or prompt engineering, on DigitalOcean Gradient GPU Droplets. Bring your own models and open-source libraries to customize fine-tuning workflows in a flexible, cost-efficient environment that scales with your needs.
Run foundation models at scale
Fine-tune open-source models like LLaMA 3, Mistral, Stable Diffusion, or YOLO using your own datasets on DigitalOcean GPU Droplets. Bring your own model checkpoints or download from public repositories, securely store them with DigitalOcean Volumes or Spaces, and iterate faster with flexible compute that scales as your workloads grow.
Use the right GPU for the job
Leverage NVIDIA GPUs (L40S, A100, RTX 6000 Ada) for tensor-intensive training, or choose AMD GPUs (MI300X, MI210) for memory-bound fine-tuning tasks with large batch sizes or longer sequences.
Multimodal fine-tuning
Run multimodal fine-tuning workloads on DigitalOcean GPU Droplets by combining text, image, and audio data to adapt open-source models for real-world tasks like visual question answering, audio grounding, or personalized recommendations. Use architectures like LLaVA or other open multimodal systems, and bring your own datasets and frameworks to create richer, cross-modal AI experiences on flexible GPU infrastructure.
Accelerate fine-tuning with DigitalOcean GradientAI GPU Droplets
DigitalOcean Gradient GPU Droplets give you direct access to powerful, elastic GPU infrastructure, purpose-built for training, fine-tuning, and deploying AI models. Whether you’re customizing a large language model, refining computer vision pipelines, or adapting multimodal systems to your own data, GPU Droplets lets you run resource-intensive workloads without managing bare-metal complexity.
Purpose-built for fine-tuning at scale
GradientAI GPU Droplets handle high-memory and high-bandwidth requirements of fine-tuning workflows on top-tier hardware.
NVIDIA GPUs: L40S/A100/H100/RTX 6000 Ada—for tensor-intensive model adaptation and large-batch processing.
AMD GPUs: MI300X/MI210/MI325X—with massive HBM capacity ideal for fine-tuning models that need long sequence context or large embeddings.
Flexible GPU scaling
Scale from 1 to 8 GPUs per Droplet to speed up training time or support large parallelized fine-tuning runs.
Gradient GPU Droplets let you scale up for large model checkpoint updates or scale down for lightweight adapters and transfer learning tasks.
High-speed networking and storage
Process massive datasets without bottlenecks.
Gradient GPU Droplets come with up to 1.5 TB GPU memory and 10 Gbps networking with attachable Volumes or Spaces for storage so you can manage model checkpoints/data pipelines/and experiment artifacts with ease.
Run multimodal fine-tuning
Support text/image and audio fine-tuning tasks in the same environment. Adapt models with your custom datasets for domain-specific outputs and edge-case handling.
Fine-tune large language models on proprietary or industry-specific datasets using DigitalOcean GPU Droplets.
Bring your own models and frameworks to improve accuracy in specialized fields like healthcare/finance/law or technical support and deploy custom-tuned models on your infrastructure.
Private, secure, and cost-effective
Keep your fine-tuning workloads secure with SOC 2 compliance, private networking, and no third-party model access required.
DigitalOcean offers predictable
transparent pricing *up to 75% cheaper than leading hyperscalers for on-demand GPU compute. *Up to 75% cheaper than AWS for on-demand H100s and H200s with 8 GPUs each. As of April 2025.
Resources to help you build
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FAQs
What is GPU fine-tuning?
GPU fine-tuning is the process of adapting a pre-trained AI model, like an LLM or computer vision model, using additional data, with GPUs to accelerate computation. Fine-tuning updates the model’s parameters (fully or partially) to improve performance on domain-specific tasks or datasets.
Which GPU is best for fine-tuning LLMs?
For LLM fine-tuning, NVIDIA A100, H100, or AMD MI300X GPUs are preferred due to their large memory capacity, high compute throughput, and optimized support for deep learning frameworks. These GPUs handle large batch sizes and long sequence lengths efficiently, reducing training time.
Can I fine-tune models with multiple GPUs?
Yes, you can fine-tune models across multiple GPUs using distributed training techniques like data parallelism, model parallelism, or tensor parallelism. Multi-GPU setups significantly reduce training time, especially for large-scale LLMs or multimodal models.
How to improve LLM fine-tuning?
To improve LLM fine-tuning results:
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Use parameter-efficient techniques like LoRA or QLoRA to reduce resource consumption.
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Ensure high-quality, domain-specific data for better generalization.
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Monitor for overfitting and catastrophic forgetting using proper validation strategies.
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Improve GPU usage with mixed-precision training and gradient checkpointing to handle larger models efficiently.
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