Training and Fine-Tuning on the OpenMetal RP6000

Q: Can I train and fine-tune AI models on the OpenMetal RP6000, or is it only for inference?

Yes, the OpenMetal RP6000 trains and fine-tunes AI models as well as serving inference; it is a general-purpose training-and-inference GPU, not inference-only.

Explore GPU servers

The RTX Pro 6000’s Blackwell tensor cores support mixed-precision training (BF16 and FP8), and its 96GB of GDDR7 holds training batches and model state on a single card. It handles training from scratch on small-to-mid models, full fine-tuning of larger models, and LoRA/QLoRA, with frameworks such as PyTorch FSDP, Hugging Face Transformers/PEFT, DeepSpeed, and NVIDIA NeMo. Pair two cards in one server, or scale to a multi-node cluster, for bigger jobs.

Training is where OpenMetal’s fixed-cost model pays off most: a job that pins the GPU at full utilization for days carries no per-hour meter and no egress bill on the data you pull back, unlike metered GPU-hour clouds where sustained training is the most expensive thing to run.

For the largest models or bandwidth-bound training, the H200 (141GB HBM3e, 4.8 TB/s) is the step up; the RP6000 is the cost-efficient workhorse for everything that fits in 96GB.