Running GPUs in 2026
A practical guide to running GPU workloads for AI/ML in 2026 across different cloud providers and environments.
Overview
This section covers how to spin up NVIDIA GPU instances and run workloads (training, inference, open-source LLMs, Jupyter, etc.) using various providers and setups:
| Provider | Region focus | GPUs | Best for |
|---|---|---|---|
| Brev by NVIDIA | Multi-cloud | L4, H100, and more via multiple clouds | One-click environments, NIM deployments, Launchables |
| Verda | Europe (Finland) | B300, B200, H200, H100, A100, RTX PRO, L4 | Latest NVIDIA GPUs, spot pricing |
| E2E Networks | India | H100, H200 | India-specific, NSE-listed hyperscaler |
| AWS | Global | P5, P4, G6, L4, A100, H100 via EC2 | Enterprise workloads, broad global reach |
| Google Cloud (GCP) | Global | A100, H100, L4 via Compute Engine | Vertex AI integration, Google ecosystem |
| My Local Machine | Your hardware | Desktop/workstation GPUs | Development, testing, on-prem workloads |
Choose a provider
- Brev by NVIDIA — NVIDIA's own platform; multi-cloud, pre-configured CUDA/Jupyter, CLI (
brev shell,brev open). - Verda — Finland-based; B300/B200 and other latest GPUs, good for spot instances.
- E2E Networks — India-focused; H100/H200 via TIR AI/ML platform, INR pricing.
- AWS — Amazon Web Services; EC2 P5/P4/G6 instances; global regions, enterprise-grade.
- Google Cloud (GCP) — Google Cloud Platform; Compute Engine GPU VMs; A100/H100/L4; Vertex AI integration.
- My Local Machine — Run GPU workloads on your own desktop or workstation; ideal for development and testing.
Pick the guide that matches your region, GPU needs, and environment, then follow the steps to create an instance (or configure your setup) and run your workloads.