cloud-gpu

This repository contains information about Cloud GPU offerings for Machine Learning practitioners.

🐍 | Python library for Runpod API and serverless worker SDK.

Run ComfyUI on Modal with auto-scaling, GPU snapshots, and easy model management. Try image, video generation via ComfyUI on Modal.

A list of Cloud GPU service providers + their prices on servers, instances, etc.

One CLI to search, provision, and manage cloud GPUs across 10 providers

Vast.ai python sdk

Train a bidirectional or normal LSTM recurrent neural network to generate text on a free GPU using any dataset. Just upload your text file and click run!

Run NVIDIA Isaac Sim's full GUI in your browser on RunPod. Isaac's built-in streaming needs UDP, which RunPod never forwards — so instead we render Isaac to a virtual display and serve it over plain HTTP with noVNC. One command, one HTTP port, no WebRTC.

Rent ready-to-use cloud GPUs in seconds. Lium CLI makes it easy to launch, manage, and scale GPU compute directly from your terminal. Fast, cost-optimized, and built for AI & ML developers.

Compare API providers, local GPUs, and cloud for any model

LTX-Video deployment on Modal — serverless AI video generation with cloud GPU scaling

iPhone capture to 3D Gaussian Splatting with COLMAP and Vulkan/VkSplat

Cloud compute tools for AI agents: provider guides, workload manifests, validated artifacts, cost controls, and cleanup for research and engineering workflows.

Run ComfyUI on Paperspace Gradient with zero manual setup — self-installing, curated custom nodes, model downloaders and presets in one notebook

Runnable recipes for fine-tuning, training, and deploying models on VESSL Cloud.

DIRECT — Codex directs ComfyUI workflows. Cloud GPUs render.

Local control panel for Paperspace Gradient GPU machines — add your API key, create/start/stop machines, open ComfyUI in one click

Advanced digital twin generator using IPAdapter FaceID with batch image embedding averaging, ControlNet integration, and LoRA training for photorealistic avatar creation

Reproducible deep-learning image-classification pipeline for industrial soft sensing. Originally built for sludge-cake quality monitoring at DC Water Blue Plains AWWTP. Six architectures (FastViT, EfficientNet, MobileNet, EfficientFormerV2, DeepTEN-ResNet, sparse-AE CNN) compared with multi-seed statistics on Modal cloud GPUs.