Axolotl is an Open Source tool to make fine-tuning AI models friendly, fast and fun - without sacrificing functionality or scale.
Users appreciate Axolotl for its simplicity and efficiency in setting up frameworks like ComfyUI, Ollama, and OpenWebUI on cloud GPUs, highlighting its ability to save time by preserving setup configurations between sessions. However, there are limited reviews available, so specific complaints about the tool haven't been widely documented. The pricing sentiment isn't clearly addressed in the available data. Overall, Axolotl is building a positive reputation among users who are looking for a streamlined process to manage complex AI installations.
Mentions (30d)
1
1 this week
Reviews
0
Platforms
2
GitHub Stars
11,556
1,284 forks
Users appreciate Axolotl for its simplicity and efficiency in setting up frameworks like ComfyUI, Ollama, and OpenWebUI on cloud GPUs, highlighting its ability to save time by preserving setup configurations between sessions. However, there are limited reviews available, so specific complaints about the tool haven't been widely documented. The pricing sentiment isn't clearly addressed in the available data. Overall, Axolotl is building a positive reputation among users who are looking for a streamlined process to manage complex AI installations.
Features
Use Cases
Industry
information technology & services
Employees
3
167
GitHub followers
18
GitHub repos
11,556
GitHub stars
20
npm packages
We built a tool that installs frameworks like ComfyUI, Ollama, OpenWebUI etc on any cloud GPU in one command and saves your whole setup between sessions [R]
We kept running into the same problem every time we rented a GPU to run Ollama + OpenWebUI or ComfyUI, we'd spend the first 45 minutes reinstalling everything. Custom nodes, models, configs, all of it. Docker images went stale fast, different providers had different base images, and nothing was truly portable. We got sick of it and built swm. Here's what it does for ComfyUI users specifically: swm gpus -g a100 --max-price 2.00 --sort price shows you the cheapest available GPU across RunPod, Vast ai, Lambda, and 7 other providers in one view swm pod create — spins up an instance on whatever provider you pick swm setup install comfyui — installs ComfyUI on the pod From there the main thing is the workspace sync. Your entire setup custom nodes, models, outputs, configs lives in S3-compatible object storage (I use B2). When you're done you run swm pod down and it pushes everything, kills the instance, and next time you spin up on any provider you just pull and everything is exactly where you left it. No more reinstalling 15 custom nodes and redownloading checkpoints every session. We also built a lifecycle guard because we kept falling asleep mid-session and waking up to dumb bills. It watches GPU utilization and if nothing's happening for 30 minutes (configurable), it saves your workspace and terminates automatically. Has saved us more money than we want to admit lol. A few other things: Background auto-sync daemon pushes changes every 60 seconds so you don't have to remember to save Tar mode for huge workspaces with tons of small files packs everything into one S3 object instead of 600k individual uploads Also supports vLLM, Ollama, Open WebUI, SwarmUI, and Axolotl if you do more than SD Works with Cursor, Claude Code, Codex, Windsurf if you want your AI agent to manage GPU instances for you Free, open source, Apache 2.0. pipx install swm-gpu Site: https://swmgpu.com GitHub: https://github.com/swm-gpu/swm Would love feedback from anyone who rents GPUs. What's the most annoying part of your current workflow? We are also looking for contributors to the open source repo and suggestions on new frameworks/extensions to be included. Please share your thoughts submitted by /u/Tkpf18 [link] [comments]
View originalRepository Audit Available
Deep analysis of axolotl-ai-cloud/axolotl — architecture, costs, security, dependencies & more
Axolotl uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Top contributors, Showcase, Sponsors, Recipes, Contact, Community.
Axolotl is commonly used for: Fine-tuning language models for specific domains, Customizing AI models for personalized user experiences, Scaling AI model deployment across multiple environments, Integrating with existing MLOps pipelines, Rapid prototyping of AI solutions using pre-made recipes, Collaborative development of AI models within a community.
Axolotl integrates with: TensorFlow, PyTorch, Hugging Face Transformers, Kubernetes, Docker, MLflow, Weights & Biases, Google Cloud AI, AWS SageMaker, Azure Machine Learning.
Axolotl has a public GitHub repository with 11,556 stars.
