- Who this is for
- Users deciding render engine and hardware path before batching jobs.
- Best fit
- Use this when a scene might render differently or fail because of engine choice, VRAM limits, or unsupported settings.
When to use this
- The workstation has a strong GPU but the scene is close to VRAM limits.
- A CPU render is slower but more predictable for the current scene.
- Several queued jobs use different scene complexity.
Workflow
- Export or collect the V-Ray Standalone scene files you want to render, usually .vrscene or .vrs files.
- Confirm that the V-Ray Standalone executable path is configured and valid on the machine that will render.
- Add the scene files to the queue, check output settings, and put jobs in the order they should run.
- Choose the useful safeguards for the job, such as frame range, skip existing frames, resumable rendering, output format, and log review.
- Start the local queue and monitor status, logs, and completed outputs from one dashboard.
Where it fits
A local queue does not choose the engine for you, but it keeps the jobs and logs easy to review after a test.
- Engine choice awareness
- GPU/CPU test workflow
- Log review
This is for local V-Ray Standalone queues. It does not provide worker provisioning, central asset sync, accounting, cloud bursting, or facility-wide scheduling.
FAQ
Should I use V-Ray CPU or GPU for queued renders?
Use the engine that matches the scene, hardware, memory limits, and expected output. Test before queueing, especially when GPU memory or feature support could be a problem.
Is this a cloud render farm?
No. V-Raykally is designed for local V-Ray Standalone queues on the artist workstation or a local render machine.
What kind of V-Ray files does this workflow target?
The workflow targets V-Ray Standalone scene files such as .vrscene and .vrs, with output and frame options handled around the local V-Ray executable.