// SOLUTIONS / FINE-TUNING
Model Fine-Tuning
Harness the power of GPU cloud infrastructure to refine your machine learning models with managed tooling and flexible compute.
Managed MLflow
Track experiments, compare runs, and manage model artifacts with fully managed MLflow — zero infrastructure overhead.
Flexible compute
Choose from 1 to 8 GPUs per instance. Scale up for larger models, scale down for parameter-efficient fine-tuning.
Data pipeline integration
Connect your datasets through Object Storage, shared filesystem, or direct S3-compatible APIs for seamless data loading.
Cost-optimized workflows
On-demand billing means you pay only for active fine-tuning time. No idle charges, no minimum commitments.
Integrated fine-tuning stack
Our platform provides everything you need to fine-tune foundation models: pre-configured environments with PyTorch and Hugging Face Transformers, experiment tracking with MLflow, and checkpoint storage on high-speed shared filesystem.
Supports LoRA, QLoRA, full fine-tuning, RLHF, and DPO workflows out of the box.
Essential resources
Compute Cloud
1 to 8 GPU instances with NVIDIA B200, H200, H100, or A100.
Managed MLflow
Track experiments and manage the ML lifecycle with zero overhead.
Object Storage
Store training data, model checkpoints, and fine-tuned weights.
Managed Kubernetes
Orchestrate fine-tuning jobs with auto-scaling GPU node pools.
Compatible tools
Hugging Face Transformers
Fine-tune any model from the Hugging Face Hub with built-in Trainer API support.
PEFT (LoRA/QLoRA)
Parameter-efficient fine-tuning for large models using minimal GPU memory.
Weights & Biases
Advanced experiment tracking, hyperparameter sweeps, and model evaluation.