Infrastructure for LoRA, QLoRA and full fine-tuning workflows.
Fine-tuning open-weight models is often the fastest path to a production-ready domain-specific model. GPUMarket provides GPU infrastructure and operational support for fine-tuning workflows built on PyTorch, Hugging Face, LoRA, QLoRA and DeepSpeed.
What this involves
- Parameter-efficient fine-tuning (LoRA, QLoRA)
- Full-parameter fine-tuning for smaller models
- Experiment tracking and reproducibility
- Dataset preparation and storage
- Multi-GPU fine-tuning with DeepSpeed or FSDP
- Evaluation infrastructure before promoting a model to production
Common challenges we help solve
Fitting fine-tuning jobs into a limited GPU budget
We recommend GPU sizing and parameter-efficient techniques (LoRA/QLoRA) to reduce memory and cost requirements.
Running many experiments without infrastructure friction
We provide on-demand GPU environments so experimentation is not blocked on capacity.
Moving from a fine-tuned checkpoint to a production endpoint
We connect fine-tuning infrastructure directly to our managed inference offerings, such as vLLM.
Recommended GPUs
Related managed software
Frequently asked questions
Do you provide the fine-tuning software, or just the GPUs?
Our core offering is GPU infrastructure and managed operations. We can also deploy and configure common fine-tuning frameworks (PyTorch, Hugging Face, DeepSpeed) on top of that infrastructure as part of a managed environment.
What GPU is best for LoRA fine-tuning?
LoRA and QLoRA significantly reduce memory requirements, often making A100 or H100 GPUs sufficient even for larger base models. We can help size this based on your specific base model and dataset.