How can I tell if jobs are GPU- or VRAM-limited?

Use tools such as nvidia-smi to check the utilization. If the VRAM (graphics memory) is full, this slows down the rendering process massively or leads to a crash. If the VRAM is okay, but the GPU is at 100%, the pure computing power is limited.

Cloud vs. On-Premise: Which is better?

On-premise (own servers) is worthwhile for permanent workloads and high security requirements. Cloud rendering is ideal for peak loads (scalability) or small studios that do not want to maintain hardware.

What software manages a render farm?

Render managers such as AWS Thinkbox Deadline, Royal Render or OpenCue control the distribution of frames to the nodes, manage priorities and error handling.

How do I keep colors consistent?

Central color management (e.g. with OpenColorIO / OCIO) is mandatory. All nodes must access the same config files so that the rendering on the farm looks exactly the same as on the workstation.

What is a render farm? Definition, types, costs & practice

Glossary

What is a render farm?

Laura Weidner

Last updated:

December 4, 2025

In this glossary article:

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Glossary

What is a render farm?

Laura Weidner

Last updated:

December 4, 2025

Definition Render Farm

A render farm is a network of many computers (nodes) that process render jobs in parallel. Instead of calculating hundreds of stills or animation frames one after the other on a workstation, tasks are distributed across many machines - production becomes calculable and fast. This is essential for series visuals and animations in furniture & interiors; we show examples and workflows in the 3D render studio and the 3D animation studio.

Types: On-Prem, Cloud, Hybrid

On-prem: full data sovereignty and constant performance - useful for permanently high workloads.
Cloud: elastic capacity for peaks, billed per computing time. A stable entry point is Azure Batch, if you want to scale workloads without your own hardware. For DCC pipelines Google Zync Render offers turnkey cloud workflows.
Hybrid: render locally and "burst" as required - in practice, this is often the best compromise between costs, time-to-market and security.

How a render farm works (practice)

Job is broken down into tasks (e.g. 240 frames).
Queue Manager distributes, monitors, prioritizes - common solutions are AWS Thinkbox Deadline, Pixar Tractor and the open source project OpenCue.
Renderer run via CLI (robust & automatable): Arnold via kick or Redshift via Command-Line Rendering.
Distributed Rendering (DBR) for heavy stills: An image is distributed to several nodes - documented in Chaos V-Ray - Distributed Rendering.

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CPU vs. GPU - which is faster?

Criterion	GPU farm	CPU farm	What does this mean for brand projects?
Time-to-First-Image / Preview	Very fast; ideal for look-dev & iteration.	Slower; previews take longer.	GPU has an advantage for quick approvals & style finding - appropriate context: Render look & material tests in the 3D render studio.
Raw throughput per node	High with well parallelizable shaders/lighting.	Stable, but usually slower per node.	Series with many stills/variants often benefit from GPU (short throughput times).
Storage budget	VRAM-limited (e.g. 12-48 GB/GPU); out-of-core slows down.	RAM-strong (128-1024 GB/node common).	Very large scenes, many high-resolution textures/displacements → rather CPU or hybrid.
Scene complexity (geometry/instancing)	Requires clean optimization (instancing, merging).	Forgive more "raw mass".	CPU is more robust for complex environments with many individual parts; GPU needs disciplined asset buildup (UV/LOD maintenance). See UV mapping.
Heavy stills (8-16 k)	Very fast with DBR/Multi-GPU if VRAM fits.	Very scalable across many cores/nodes.	Key visuals: GPU (DBR) or CPU (many cores). Decision often dependent on VRAM.
Long animations	Fast per frame, but VRAM risks & driver maintenance.	Very stable/deterministic, large RAM cushion.	CPU or hybrid is more plannable for longer spots/explanatory videos. More on this in the 3D animation studio.
Shader/feature parity*	Very high today; individual special cases (AOVs/volume) depending on the engine.	Almost "anything goes", broad feature set.	For special effects/volumes, check parity if necessary; test GPU beforehand.
Denoising	OptiX/AI-Denoiser very fast in the iteration loop.	OIDN/CPU denoiser high quality, slightly slower.	Clear GPU advantage for release previews; final clean rendering & fine denoising.
Costs (CapEx / OpEx)	Higher costs per GPU hour, but top performance; can be burst very well in the cloud.	Cheaper vCPU hours, hardware versatility.	Control budget mix: GPU for peaks/series, CPU as "workhorse". Spot/Preemptible in the cloud helps.
Energy efficiency	Very good "per frame" when utilization is high.	Solid; varies depending on core count/cycle.	GPU is often more efficient during campaign sprints, but this is put into perspective under continuous load.
Maintenance & operation	More sensitive (GPU drivers, VRAM limits).	Less critical, easier to standardize.	In small teams, CPU scores points for maintainability; GPU requires stricter pipeline rules.
Scaling via nodes	Very good; linear scaling with many independent frames.	Very good; classic farm use for years.	Both scale strongly - "shard" frames, prioritize queue cleanly.
Typical engines (examples)	Redshift, Octane, V-Ray GPU, Cycles GPU, Unreal (real-time)	Corona (CPU), V-Ray CPU, Arnold CPU, Cycles CPU	Selection influences the choice of farm - the glossary explains the basics Render Engine & Ray Tracing.
Asset requirements	Strict optimization (LODs, Atlas textures, instancing).	More tolerant; still benefits from optimization.	Mandatory for Web/AR: clean PBR sets& bakes - see Texture Baking.
Best-fit use cases	Series stills, variants, quick look-dev loops, 360° spins.	Large environments, memory-hungry shots, long animations.	For furniture/interior often: Hybrid - GPU for series & iteration, CPU for "thick" scenes/sequences.

* Feature parity is engine-dependent. Before starting the project, render the desired AOVs/volumes/hair/dispersion as a test and check with your ACES/EXR setup (color space & layer structure).

Brief conclusion:

Fast & iterative (series stills, variants, previews) → GPU has an advantage.
Large & complex (lots of geometry/volumes, long sequences) → CPU or hybrid.
For consistent campaign looks: Set up a clean color space/compositing (ACES + OpenEXR) and maintain material standards (PBR).

Pipeline fit for furniture & interior

Series with variants (fabrics, woods, handles), 360° spins or milieu campaigns benefit massively because their timing remains predictable. You can find practical references in our cases on Natuzzi, Bretz and interlübke.

Ensure quality: Color, formats, materials

Standardize color space & tone mapping with ACES - Academy Color Encoding System for consistent looks across campaigns, devices and renderers.
Ensure compositing flexibility with OpenEXR (multi-layer EXR for passes/lightmix).
PBR material standard (Albedo/Roughness/Metalness) for repeatable results - basics in our glossary articles on PBR - Physically Based Rendering and Metalness Map.

Realistically calculate costs & planning

Billing in node hours (CPU/GPU), plus manager license, render licenses, storage/traffic.
Saving tips for batch jobs: AWS EC2 Spot Instances and Google Compute Engine Spot VMs offer significant discounts if your queue tolerates interruptions.
Allow a buffer (10-20%) for re-renders (look tweaks) and transfers for cloud setups.

Selection checklist for brands & manufacturers

Define target image: Photorealistic still (possibly DBR) vs. longer animations - basics of shading and light in render engine and ray tracing.
Volume & deadline: Derive number of images/frames × date → farm size or cloud bursting.
Security: NDA, data residency, encryption; limit retention in cloud queues.
Pipeline: CLI render (e.g. Arnold kick, Redshift Command Line), versioning, asset caching.
Cost control: Spot/Preemptible, monitoring (node utilization, requests), clean job prioritization.
Look-Dev-Guardrails: ACES + OpenEXR + PBR as fixed guardrails.

FAQ - Render Farm

How can I tell if our jobs are GPU- or VRAM-limited?

During the rendering process nvidia-smi provides you with live values on utilization and available graphics memory; if the memory utilization reaches the limit, VRAM is the bottleneck.

Which denoiser is suitable for farm pipelines with high image quality?

For CPU-side denoising Intel Open Image Denoise has proven itself; the denoiser can be easily automated and is integrated in many DCCs/renderers.

How do I keep color and tone mapping consistent across all render nodes?

Central color management with OpenColorIO ensures that workstations and farms use the same config - so looks remain stable across campaigns and renderers.

Which exchange format is the most robust for large productions?

Scaled for scene-based exchange and layout/lighting OpenUSD scales very well; references, variants and layering can be versioned cleanly.

Does headless rendering work reliably without a GUI?

Yes - automated queue jobs run stable via command line; the Blender render arguments show typical switches for frames, devices and output.

How do I control dependencies and batches in complex pipelines?

Procedural task graphs simplify orchestration; PDG/TOPs in Houdini models dependencies, distributes caches and renders in parallel on the farm.

How do I avoid "dependency hell" with tool versions?

Align your environment with the VFX Reference Platform ; the vintage stack (including compiler, Python, Qt) ensures reproducible builds in mixed toolchains.

Does multi-GPU with NVLink provide more usable memory?

NVIDIA NVLink does not bundle VRAM into a single pool, but accelerates peer-to-peer transfers between GPUs - helpful for workloads that require a lot of data exchange between cards.