iwant

About iwant

iwant is a command-line tool that automates the deployment of open AI models on Google Cloud Platform (GCP). It locates available GPU capacity across regions, provisions the infrastructure, downloads model weights, and configures a vLLM serving endpoint. Users receive an OpenAI-compatible API endpoint, an API key, and SSH access - all running within their own cloud account.

Review

iwant tackles a specific friction point: turning a model experiment into a working API often means manually checking GPU availability across zones, handling provisioning scripts, and configuring inference servers. The tool compresses those steps into a few commands. It's a focused utility that doesn't try to be a full MLOps platform.

Key Features

• Automated GPU hunting: iwant scans GCP regions to find available GPU capacity, eliminating manual zone-by-zone checks.
• One-command model deployment: The iwant up command provisions a machine, downloads weights, and sets up vLLM with a single instruction.
• Model recipes: Pre-configured recipes abstract hardware requirements and serving settings for specific models like DeepSeek, Kimi, Minimax, MiMo, and Step.
• Cluster management: iwant list shows running deployments and their connection details, while iwant down tears down clusters.
• Dry-run inspection: The --dry-run flag lets users review the planned infrastructure before committing resources.

Pricing and Value

iwant itself is free and open source. Users pay only their own GCP infrastructure costs - compute, GPU, and networking charges billed directly by Google Cloud. The tool works with existing GCP credits. No pricing tiers or paid plans are mentioned in the available information.

Pros

• Runs entirely in the user's GCP project, keeping data and infrastructure under their control.
• Reduces multi-step GPU provisioning to a single command.
• Exposes an OpenAI-compatible endpoint, so existing client code works without modification.
• Model recipes handle hardware selection, removing guesswork about GPU type and count.
• Source code is open, allowing inspection and modification.

Cons

• GCP is the only supported cloud provider at launch; AWS and Azure users cannot use the tool.
• Users need pre-approved GPU quota and billing enabled - the tool doesn't help obtain or increase quotas.
• The tool is not well suited for teams running production workloads that require autoscaling, persistent monitoring, or multi-node orchestration beyond what a single vLLM instance provides.

iwant fits developers and researchers who want to test open models quickly without writing Terraform scripts or clicking through cloud consoles. It's most practical for short-lived experiments, demos, and evaluation workflows where spinning up and tearing down GPU instances on demand matters more than long-running service management. Teams with multi-cloud needs or strict production requirements will likely need additional tooling alongside it.