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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsVelo Workspaces’ recommended way to run a coding agent on a Mac is to split the work. The agent and its tools live inside a Linux guest VM. The local model runs on the macOS host and is reached through Velo’s AI Bridge. “Zero-tax” is Velo’s name for the goal: you get VM isolation without paying the speed penalty of running model inference inside the VM.
This article explains that architecture, the connection pattern the guide describes, which agents and model backends it covers, and what is vendor claim rather than established fact. Everything here comes from Velo’s own guide and product page. None of it has been independently benchmarked or audited, so claims are attributed to Velo throughout.
The architecture in one picture
Velo’s guide divides the job between two machines that share one piece of hardware:
| Layer | Where it runs | What it does |
|---|---|---|
| Model server | macOS host | Runs MLX or Ollama and does the inference |
| AI Bridge | Between host and guest | Carries model requests over VSOCK |
| Guest proxy | Ubuntu Linux guest | Exposes the host model as a local port at 127.0.0.1 |
| Agent framework | Ubuntu Linux guest | Runs commands, installs dependencies, edits files |
The reasoning is that a coding agent executes shell commands, installs packages and modifies files. If that happens in a VM, the agent’s direct reach is limited to the guest rather than your Mac. Meanwhile the GPU-heavy work stays on the host, where Velo says it runs at full speed.
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Treat this as Velo’s recommended design, not an independently certified isolation model.
Why not just run the model inside the VM?
Velo’s guide asserts that inference inside a Linux VM can cut generation speed by “80%+”. That is the “tax” the title refers to. The guide also says the vsock bridge adds only “single-digit milliseconds” of overhead. Both figures are Velo’s. No test hardware, model, workload or method was published alongside them in the material reviewed, so read them as the vendor’s characterization, not as measurements you should expect to reproduce.
The practical takeaway is narrower than the marketing: if you want a local model and an isolated agent, the bridge design avoids running the model in the guest at all. Whether your own speeds match Velo’s claims depends on your Mac, model and workload.
Choosing a host backend: MLX or Ollama
The guide supports two host-side model servers. It does not offer a controlled speed comparison between them, so choose on setup and model availability.
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| MLX | Ollama | |
|---|---|---|
| How Velo describes it | Apple Silicon-native inference | Simple, one-command setup |
| Port in the guide’s example | 8080 | 11434 |
| Models | MLX-formatted models | Models from Ollama’s own library |
| Speed versus the other | Not stated | Not stated |
How the connection works
1. Prepare the guest and the bridge
In Velo, the guide has you configure an AI Sandbox profile, turn on AI Bridge, pick the host provider (MLX or Ollama), and install the guest proxy. Exact menu labels may change between app versions, so follow the current in-app wording.
2. Understand the proxy
The guest proxy uses socat. It listens on the model’s port at 127.0.0.1 inside the guest and forwards connections to VSOCK host CID 2, the host, on the same port. A command of this general shape matches that description for MLX:
socat TCP-LISTEN:8080,bind=127.0.0.1,fork,reuseaddr VSOCK-CONNECT:2:8080This is an illustration of the described pattern, not a copy of Velo’s exact command. Use 11434 in both places for Ollama. Binding to 127.0.0.1 means other machines on the guest’s network cannot reach the forwarded model port.
3. Verify from inside the guest
The guide’s check is to request http://127.0.0.1:<PORT>/v1/models from the guest, for example with curl. A list of models means the host server is reachable. If it fails, check in order: the model server is running on the host, the bridge is enabled for the right provider, the proxy is running, and the ports match.
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Agents the guide covers
Velo gives configuration examples for four agents, all pointing at an OpenAI-compatible local endpoint:
- OpenCode: a custom OpenAI-compatible provider.
- Open Interpreter: a local API base setting.
- Aider: the
OPENAI_API_BASEenvironment variable plus an OpenAI-compatible model prefix. - Goose: its custom provider configuration.
In each case the base URL is the guest-local address, such as http://127.0.0.1:8080/v1 for MLX. Flags, config keys and provider names in these tools change often, so treat the guide’s snippets as a starting point and check each tool’s current documentation.
What “safely” does and does not mean
A VM boundary is a real, widely used way to limit what a misbehaving process can touch. The guide’s argument is that it limits the guest’s direct access to the host. But the material behind this article does not verify the complete boundary. In particular, it does not establish:
- how shared folders, clipboard or other host/guest sharing are configured by default;
- what network access the guest has, which matters because an agent with network access can still send out whatever is inside the VM;
- protection against prompt injection, where hostile text steers the agent into harmful actions within its permitted scope;
- resistance to hypervisor-level attacks.
Practical habits follow from this: keep sensitive files out of any folder shared with the guest, give the agent only the credentials the task needs, and review shared-folder and network settings in your profile before starting.
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Privacy: what Velo says
Velo’s product page states: “Nothing is sent to Velo Workspaces or any third party.” It also says no usage data or crash reports are collected. These are vendor statements, not an independent audit. They concern Velo’s app and local bridge; they cannot cover what a third-party agent, extension or model does if you configure it to call a remote service.
Cost and access
The product page lists AI Bridge as a Pro feature, not part of the free tier. Pricing seen on 2026-10-05:
- $3.99 per month
- $24.99 per year
- $79.99 once, for life
- A seven-day free trial
Prices can change, so confirm on Velo’s site. The trial is a low-risk way to test the setup on your own models.
Hardware requirements
Velo says the app is built for Apple Silicon, with Linux guests also running on Intel Macs. The source names no minimum chip, memory size or recommended Mac model. Model size is the practical constraint, since the host must hold the model in memory while also running macOS and the guest. If your current Mac already runs your chosen model comfortably, nothing in the guide suggests you need new hardware.
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