GPU Rendering Explained (2026): How It Works, VRAM Limits and Multi-GPU

GPU rendering has become the default for many 3D artists using Redshift, Octane, Cycles or Karma XPU. It is fast, but it comes with rules that catch people out: the scene must fit in VRAM, extra GPUs do not add up their memory, and each engine supports different hardware. This guide explains how GPU rendering works, using the renderers’ own documentation.

Checked against the Cinema 4D and Redshift documentation, November 2026.

GPU rendering uses the graphics card instead of the CPU to calculate the image. According to the Blender manual, this can speed up rendering because modern GPUs are designed for heavy number crunching, while their more limited memory restricts how complex a scene can be. For a side-by-side comparison, see CPU vs GPU rendering.

Sources: Blender manual: GPU rendering, Maxon: Redshift requirements.

Four GPU engines cover most multi-GPU work today: Redshift and Octane in Cinema 4D, Houdini and other apps, Cycles in Blender, and Karma XPU in Houdini Solaris. All four render on NVIDIA GPUs and treat each card’s memory separately.

Sources: Maxon support, Maxon: Redshift requirements, OTOY: Devices, OTOY: Out-of-Core, Blender manual, SideFX: Karma XPU.

• Octane: OTOY recommends disabling GPUs that do not have enough VRAM so a large scene can fit on the remaining cards. See Octane on iRender.
• Karma XPU: combines OptiX GPU devices with an Embree CPU device, and Houdini can show each device’s share of the image. On iRender, Karma XPU runs best on 4-GPU servers; 8 GPUs add little. See Houdini on iRender.
• Redshift and Cycles: see Redshift multi-GPU rendering and CPU vs GPU rendering.

Geometry, textures and render buffers all have to sit in the GPU’s memory. When the scene is too big, Cycles tries system memory automatically with a performance cost (Blender manual); Redshift uses out-of-core memory, see Redshift out-of-core. Octane can keep textures and geometry in system RAM with its out-of-core option (OTOY), and Karma XPU lets the other devices, including the CPU, finish the frame if a GPU runs out of memory (SideFX). Textures are often the biggest item:

No, in most cases. The Blender manual states each GPU can only access its own memory. Extra GPUs make rendering faster, but the scene must still fit on each card. OTOY states the VRAM available to Octane is that of the smallest GPU (OTOY), and SideFX states Karma XPU addresses each GPU separately and does not pool memory. See Redshift multi-GPU rendering and multi-GPU render engines for Cinema 4D.

Settings checklist (copy or follow line by line):

Blender (Cycles)
  Preferences › System › Cycles Render Devices .. OptiX (RTX) or CUDA
  Tick ........................................... every GPU
  Properties › Render › Device ................... GPU Compute

Cinema 4D (Redshift)
  Edit › Preferences › Renderer › Redshift
    Compute Devices .............................. tick every GPU
  Render Settings › Renderer ..................... Redshift

Octane
  Preferences › Devices › Render ................. tick every GPU
  Preferences › Application › Out-of-Core ........ on for large scenes

Houdini (Karma XPU)
  KARMA_XPU_DEVICES ............................... optix = GPU devices only

Check ............................................ GPU load during a test frame

Step 1: Choose the backend

In Blender, open Preferences › System and pick OptiX or CUDA under Cycles Render Devices (Blender manual).

Step 2: Enable the GPUs

Tick every GPU you want to use. In Cinema 4D, do this under Compute Devices in the Redshift preferences (Maxon).

Step 3: Set the scene to GPU

In Blender, set Properties › Render › Device to GPU Compute. In Cinema 4D, choose Redshift as the renderer.

Step 4: Test a frame

Watch GPU load and memory in Task Manager or nvidia-smi while rendering one frame.

A SaaS render farm takes your scene as a job and picks the hardware. An IaaS render farm such as iRender gives you a remote server with 1 to 8 NVIDIA GPUs where you choose the engine, backend and devices yourself. See SaaS vs IaaS render farm.

GPU rendering on iRender multi-GPU servers.

On iRender you rent a whole server with 2x, 4x or 8x RTX 5090 or RTX 4090 GPUs, with Cinema 4D and Redshift pre-installed; Prime members get activated Cinema 4D and Redshift licenses. Real render times are in our Redshift benchmark, and plans are on the Cinema 4D Redshift render farm page.

What is GPU rendering?
GPU rendering uses the graphics card instead of the CPU to calculate rendered images. Modern GPUs are designed for heavy parallel computation, which speeds up ray tracing.

Is GPU rendering better than CPU rendering?
Often faster, but limited by GPU memory. For a full comparison see our CPU vs GPU rendering guide.

Do multiple GPUs add up their VRAM?
Typically no. The Blender manual states each GPU can only access its own memory, and Maxon support says Redshift does not combine VRAM across GPUs.

What happens when a scene does not fit in VRAM?
It depends on the engine. Cycles with CUDA, OptiX, HIP and Metal automatically tries system memory, which is slower but usually still faster than CPU. Redshift and Octane offer out-of-core options.

Which GPU is best for rendering?
It depends on the engine: Redshift and Octane on Windows and the GPU device of Karma XPU need NVIDIA GPUs, while Cycles supports NVIDIA, AMD, Intel and Apple. More VRAM lets you render heavier scenes.

Can I use GPU rendering without buying a GPU?
Yes. An IaaS render farm such as iRender rents you a remote machine with 1 to 8 NVIDIA GPUs where you install or use the pre-installed software.