Running AI agents in KVM VMs solves the transient execution problem for large-scale workflows
Local sessions fail under real-world demands: a coding agent scanning a codebase or training reinforcement learning models will inevitably crash when memory fills, security risks accumulate, or network connectivity drops. Even running with --yolo exposes credentials to leaks, and hardware limitations force abrupt termination.
This solution bypasses containers and sandboxes, using KVM virtual machines instead. The approach provides direct kernel access and full GPU driver support without the overhead of syscall interception, preserving performance.
Integration begins with a single command:
machine0 new myboxThis creates a VM with a static IP and HTTPS endpoint. Once deployed, the agent operates independently—spawning build environments, saving snapshots, and shutting down automatically after completing a pull request.
Hardware starts at 1 vCPU and 1 GB RAM ($0.013 per hour) and scales to 60 vCPU and 240 GB RAM. GPU options include RTX 4000 Ada up to eight H200s, with block storage ranging from 10 GB to 16 TB. VM reliability reaches 99.99 percent uptime.
The Profiles feature ensures security by bundling credentials, model connections, and environment variables into a task-specific configuration. The agent receives only what it needs, eliminating unnecessary permissions.
Early users describe two common patterns: deploying a pilot agent to analyze a project before distributing subtasks to specialized worker VMs, or using suspended H100 instances for overnight model optimization. The result is a system that scales without local execution limits.
For production use, deploying NixOS flakes or Ansible playbooks on these VMs ensures consistent environments—a common bottleneck when moving agents from development to deployment.
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This feels like a Mac-specific issue. Which power setting actually fixes the sleep cycle? As a concrete alternative, launch a box with machine0 new mybox. This provisions a static IP and HTTPS endpoint.
Are you running this on AWS or a self-hosted environment? I need to know the infrastructure setup. The fundamental flaw in most current agent architectures is their transient nature. Running a coding agent across a massive codebase or conducting RL training cannot depend on a local session alone. You encounter RAM saturation, security exposure — executing --yolo on your own hardware practically invites credential leaks — and the inevitable crash when WiFi falters. Why full KVM virtual machines beat containers The distinctive approach here avoids containers or sandboxes entirely, opting for full KVM virtual machines. This delivers genuine kernel-level access and authentic GPU drivers without a syscall-interception layer degrading performance. How to launch an agent in one command For a practical integration into an AI workflow, setup reduces to a single command. Launch a box with:
machine0 new myboxThis provisions a static IP and HTTPS endpoint, and from that point the agent operates autonomously. Grant a tool such as Claude Code or an MCP server CLI access, and the agent manages its own infrastructure — provisioning a build box, snapshotting state, and tearing down once the PR is ready. Hardware range begins at 1 vCPU / 1 GB RAM ($0.013/hr) and extends to 60 vCPU / 240 GB RAM. GPU support spans RTX 4000 Ada through 8×H200s. Persistence covers block storage from 10 GB to 16 TB. Uptime reaches 99.99 percent VM-level reliability. Why this matters for LLM agents The decisive advantage is the Profiles feature. Credentials, MCP connections, and environment variables bundle into a profile inj
A basic cron job usually stops my system from sleeping. The fundamental flaw in most current agent architectures is their transient nature. Running a coding agent across a massive codebase or conducting RL training cannot depend on a local session alone. Which keep-awake app are you using? You encounter RAM saturation, security exposure — executing
--yoloon your own hardware practically invites credential leaks — and the inevitable crash when WiFi falters. The distinctive approach here avoids containers or sandboxes entirely, opting for full KVM virtual machines. This delivers genuine kernel-level access and authentic GPU drivers without a syscall-interception layer degrading performance. The technical breakdown.