Fine-Tune AI Models on Dedicated GPUs
Adapt open-source foundation models using your own datasets without managing infrastructure.
From base model to custom assistant
Launch GPU environments optimized for LoRA, QLoRA, PEFT and full fine-tuning workflows.
Train custom assistants, enterprise copilots and domain-specific AI models faster.
Infrastructure and outcomes that matter
Fast storage
NVMe-backed storage for datasets and checkpoints.
Checkpointing
Resume safely from interruptions without losing progress.
Distributed training
Scale a fine-tuning job across multiple GPUs when needed.
GPU scaling
Add capacity as datasets or models grow.
Spot & dedicated instances
Balance cost and reliability to fit your budget.
Lower training cost
Pay only for what a fine-tuning run actually needs.
Fast iteration
Short feedback loops between experiments.
Secure environment
Your data and weights stay isolated to your project.
Right-sized for adaptation, not pre-training
H100
RTX 4090
L40S
How a fine-tuning job runs
Fine-tuning jobs run in isolated environments with fast NVMe storage for datasets and checkpoints, automatic checkpointing for safe interruption and resume, and the option to mix spot and dedicated capacity to control cost.
Base Model + Dataset
Your data, your weights
Fine-Tuning Job
LoRA, QLoRA or full fine-tune
Deployed Model
Ready for inference
Pick the approach that fits your model and budget
LoRA
Lightweight adaptation with a small set of trainable parameters.
QLoRA
Quantized LoRA for fine-tuning larger models on less hardware.
PEFT
Parameter-efficient fine-tuning methods beyond LoRA.
SFT
Supervised fine-tuning on labeled instruction data.
RLHF preparation
Infrastructure to prepare data and reward pipelines for RLHF.
Continued Pre-training
Extend a base model's knowledge before task-specific tuning.
Built for fast, affordable iteration
- Lower training cost than pre-training from scratch
- Fast iteration between experiments
- Easy scaling as datasets grow
- Secure, isolated environments
Built to fit your existing stack
Every deployment ships with API access and console control, so you can automate provisioning or hand it to your engineer of record.
Full API reference and setup guides are shared during onboarding — reach out and we'll get you access.
Talk to an Expert