Fine-tune without booking a cluster
Bring your trainer and your dataset. Meshive gives you a whole GPU with the VRAM headroom the run actually needs, a volume that survives restarts, and billing that stops when the job does.
Unsloth and Axolotl are already in the image
The trainer, CUDA and the Python stack ship with the template. You are not building a container before the first run.
VRAM decides what you can train
96GB on an RTX PRO 6000 holds a 70B LoRA without offloading. 32GB on a 5090 is comfortable for 8B. Pick by the model, not by the price.
An interrupted run resumes
Checkpoints go to a volume that outlives the pod, so a stop costs you the compute since the last save and nothing else.
Templates for this work
Official images with the drivers and the stack already in place.
- Unsloth
- Axolotl
- LLaMA-Factory
- AI-Toolkit
- Jupyter
Cards that suit this work
Every Meshive pod gets a whole, non-virtualized GPU. These are the ones we would reach for first.
- RTX PRO 6000 Blackwell70B LoRA fits in 96GB without offloading
- A100 PCIe 80GBProven on long multi-day runs
- RTX PRO 5000 Blackwell8B-13B full fine-tunes at 48GB
- RTX 50908B LoRA and short experiments
Three steps
- 1Start from the Unsloth or Axolotl template - the trainer and CUDA stack are already installed.
- 2Attach a volume and point your checkpoint directory at it.
- 3Run the trainer over SSH or in the notebook. Stop the pod and billing stops with it.
Also on Meshive
Pick a card and start the pod
Sign up, choose the GPU, and the pod is yours in under two minutes. It bills by the hour and stops when you stop it.
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