--- title: "Run Axolotl fine-tuning on a cloud GPU from $0.163/hr | PowerGPU" description: "Axolotl — Fine Tuning on a fixed-price cloud GPU: one-click template, running in ~30 s, from $0.163/hr on RTX 4090. Best GPUs, volumes, per-second billing." url: https://powergpu.ai/templates/axolotl-fine-tuning last_modified: 2026-09-14T11:04:20+00:00 prices_as_of: 2026-09-14 site: PowerGPU (powergpu.ai) --- Template · Training & fine-tuning · CUDA 12.6 # Run Axolotl fine-tuning on a cloud GPU, in 30 seconds Fine-tune Llama, Qwen or Mistral from one YAML — LoRA, QLoRA and multi-GPU FSDP. Axolotl fine-tunes Llama, Qwen, Mistral and friends from one YAML: LoRA, QLoRA, full-parameter, DeepSpeed and FSDP, with dataset formats handled for you. The template is what our QLoRA walkthrough is written against — checkpoints go to a volume, the trainer instance is disposable. From **$0.163** /hr on an interruptible RTX 4090. Axolotl — Fine Tuning axolotlai/axolotl-cloud (CUDA 12.6) (ARM) (SSH) (Jupyter) Pinned image, rebuilt on upstream releases. Override env, ports and disk at deploy. ### Running in ~30 s Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this. ### Template is free You pay the GPU price only: Axolotl — Fine Tuning on a RTX 4090 is $0.327/hr on-demand, $0.163/hr interruptible, billed per second. ### Volumes for state Models, datasets and outputs live on a $0.08/GB/mo volume; the instance stays disposable. ### Private by default Dedicated GPU, encrypted disk, crypto payments, no KYC, no stored IPs — Jupyter and SSH behind your own credentials. ## Best GPUs for Axolotl — Fine Tuning Three price points that run this template well — Good, Better, Best. Every model in the catalogue works; these are the value picks. | Tier | GPU | VRAM | On-demand | Interruptible | Why this card | | | --- | --- | --- | --- | --- | --- | --- | | (Good) | [RTX 4090](https://powergpu.ai/gpu/rtx-4090) | 24 GB | $0.327 | $0.163 | QLoRA 8B–13B on 24 GB for a couple of dollars. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-4090) | | (Better) | [A100 SXM4](https://powergpu.ai/gpu/a100-sxm4) | 80 GB | $0.560 | $0.280 | 80 GB with NVLink for full fine-tunes and 8× FSDP. | [Deploy](https://cloud.powergpu.ai/?gpu=a100-sxm4) | | (Best) | [H100 PCIE](https://powergpu.ai/gpu/h100-pcie) | 80 GB | $1.867 | $0.933 | 80 GB + FP8 — full fine-tunes in roughly half the A100 wall-clock. | [Deploy](https://cloud.powergpu.ai/?gpu=h100-pcie) | Need more VRAM? The [full catalogue](https://powergpu.ai/gpus) lists all 80 models with live availability; the [VRAM guide](https://powergpu.ai/guides/llm-vram-requirements) sizes models to cards. ## Deploy Axolotl — Fine Tuning from the console, CLI or API Pick the template in the [console](https://cloud.powergpu.ai/) deploy bar, or script it: - **Console** — filter by GPU, choose *Axolotl — Fine Tuning* in the template picker, set disk and env, deploy. - **CLI** — pip install powergpu, then the command on the right. [CLI reference](https://powergpu.ai/cli). - **API** — POST /v1/instances with "template": "axolotl-fine-tuning". [REST reference](https://powergpu.ai/api). - **Own image** — any OCI reference works too; we inject the NVIDIA runtime. [Template docs](https://powergpu.ai/docs/templates). *deploy — axolotl-fine-tuning* ``` $ powergpu launch --gpu rtx-4090 --template axolotl-fine-tuning \ --disk 100 --volume models:/workspace/models ✓ instance i-7a41c0e2 running (27.9s) # Axolotl — Fine Tuning · RTX 4090 · $0.327/hr · per second # https://i-7a41c0e2.powergpu.ai:8888 (TLS) $ powergpu stop i-7a41c0e2 # billing ends this second ``` ## What is inside the Axolotl — Fine Tuning template - **Image**: axolotlai/axolotl-cloud - **CUDA**: CUDA 12.6 - **Access**: also builds for ARM hosts · SSH shell · JupyterLab on a mapped port - **Category**: [Training & fine-tuning](https://powergpu.ai/templates#tune) - **Storage**: Instance NVMe disk (sized at deploy) + optional [network volumes](https://powergpu.ai/products/volumes) - **Billing**: GPU price only, per second — no template fee, no setup fee ## Other training & fine-tuning templates - [Unsloth Studio on a cloud GPU](https://powergpu.ai/templates/unsloth-studio) — 2× faster, lower-VRAM fine-tuning — Unsloth kernels with a notebook workflow. - [Kohya's GUI on a cloud GPU](https://powergpu.ai/templates/kohya-s-gui) — Train SDXL and Flux image LoRAs in a browser GUI — dataset in,.safetensors out. - [Flux Gym on a cloud GPU](https://powergpu.ai/templates/flux-gym) — A dead-simple UI for training Flux LoRAs, built on the Kohya scripts. - [Ostris AI Toolkit on a cloud GPU](https://powergpu.ai/templates/ostris-ai-toolkit) — The AI-Toolkit trainer for Flux and diffusion models, with a web UI. - [All 37 templates](https://powergpu.ai/templates) ## Axolotl — Fine Tuning on a cloud GPU: FAQ Environment variables, ports and custom images are covered in the [template docs](https://powergpu.ai/docs/templates). **How much does it cost to run Axolotl — Fine Tuning on a cloud GPU?** Only the GPU price — the template is free. From $0.163/hr on an interruptible RTX 4090, $0.327/hr on-demand on a RTX 4090. Billing is per second, so an hour of tinkering costs an hour, not a day. Storage is $0.08/GB/month. **How long does Axolotl — Fine Tuning take to start?** About 30 seconds from the deploy click: the image is pre-cached on hosts, ports are mapped and TLS is terminated for you. Restarting a stopped instance is faster, and your disk is exactly as you left it. **Can I keep my models and outputs between sessions?** Yes — attach a volume at deploy. Everything on the volume survives instance destruction and mounts on the next instance in the region in seconds, at $0.08/GB/month. The instance disk itself survives stop/start but not destroy. **How long does a QLoRA run take?** About 1.5 hours for 10k instruction pairs on Llama 3.1 8B on one RTX 4090; a 70B QLoRA on an 80 GB card is an overnight job. The guide lists exact configs and bills. --- *About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API. Source: https://powergpu.ai/templates/axolotl-fine-tuning · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt