Redshift Multi-GPU Rendering in Cinema 4D: How It Scales, VRAM and When More GPUs Pay Off
Redshift renders faster with more GPUs, but only speed is shared, not memory. The main points from Maxon's documentation:
- Speed is shared: All GPUs in a machine can work on the same frame by splitting it into buckets; Maxon notes the gain can be non-linear.
- Memory is not: Maxon support: Redshift does not combine VRAM across GPUs. Four 32 GB cards still mean a 32 GB in-VRAM limit.
- Two modes: Render each frame on all GPUs, or render several frames at once on subsets of GPUs; long, light animations often scale better the second way.
- Measured jobs: On iRender, 150 frames rendered in 1h 42m on 4x RTX 5090 and 360 frames in 1h 18m on 8x RTX 4090 (two different scenes).
- On iRender: 2x, 4x and 8x RTX 5090 or RTX 4090 servers with Cinema 4D and Redshift pre-installed; split long sequences across several servers for deadlines.
Adding GPUs is the fastest way to speed up Redshift, but it does not work the way many people expect. More cards share the work of each frame; they do not add up their memory. This guide explains how Redshift uses several GPUs, what limits scaling, and how to choose between 2, 4 and 8 GPUs, based on Maxon’s documentation and recorded iRender jobs.
Checked against the Cinema 4D and Redshift documentation, November 2026.
1. How does Redshift use multiple GPUs?
Redshift splits each frame into buckets and hands them to the available GPUs, so all cards work on the same frame (Maxon: hardware recommendations). The scene itself is not split: each GPU needs its own copy in its own memory.
On the left, the frame is shared out bucket by bucket, which is why more GPUs make each frame faster. On the right, each card holds the whole scene, which is why four 32 GB GPUs still give a 32 GB scene limit. Maxon states it directly: Redshift does not combine VRAM when using multiple GPUs (Maxon support).
Two ways to use several GPUs with Redshift
From Maxon's CPU, GPU and hardware recommendations for Redshift.
| Mode | How it works | Best for |
|---|---|---|
| All GPUs on each frame | Redshift splits the frame into buckets and every GPU renders part of it. Maxon notes the gain can be non-linear. | Heavy frames, stills, look-dev and test renders |
| Several frames at once | Separate render processes, each using a subset of the GPUs, render different frames in parallel. | Long animations of light frames where per-frame overhead dominates |
| Several servers | Split the frame range across machines; each renders its own frames. | Deadlines; turnaround drops roughly with the number of servers |
A note on sources about VRAM: Maxon’s support article states that Redshift does not combine the VRAM when using multiple GPUs. Maxon’s 3D rendering product page also lists “multi-GPU memory pooling” and says Redshift shares out-of-core memory across multiple cards. This article follows the support article: plan for every GPU holding the scene in its own VRAM. Check Maxon’s latest documentation for your Redshift version.
2. Why doesn't Redshift scale perfectly with more GPUs?
Part of every frame is work the GPUs cannot share, and some settings affect how evenly work is spread. Maxon notes that rendering a single frame on all GPUs can give a non-linear gain, and that large buckets can reduce multi-GPU performance unless the image is high resolution (Maxon: System settings).
What decides how well Redshift scales
From Maxon documentation; check each factor on your own scene.
| Factor | Why it matters | What to do |
|---|---|---|
| Scene preparation time | Exporting and preparing the scene happens before the GPUs start each frame and does not get faster with more GPUs. | Check the per-frame log; cache heavy deformers and simulations |
| Bucket size | Maxon notes large buckets can reduce performance with multiple GPUs unless the frame is high resolution; default is 128x128. | Keep the default unless you have a reason |
| VRAM per GPU | VRAM is not combined across GPUs; out-of-core data is slower. | Watch the memory in the Feedback Display |
| Mixed GPUs | Each card must still hold the scene, so the smallest card sets the in-VRAM limit. | Use matching GPUs for heavy scenes |
3. How to choose between 2, 4 and 8 GPUs (step by step)
Settings checklist (copy or follow line by line):
1. Does the scene fit one GPU? Redshift Feedback Display ... geometry + texture memory > 24 GB ..................... use 32 GB RTX 5090 GPUs 2. Time 2–3 representative frames 2 GPUs (4S / 4i) ............ seconds per frame = A 4 or 8 GPUs (5i / 9i) ....... seconds per frame = B speed-up = A ÷ B (2× GPUs giving far less than 2× = high per-frame overhead) 3. Cost per frame = seconds per frame ÷ 3600 × hourly price 4. Long, light sequences ...... several frames at once (GPU subsets or servers) 5. Full job ................... best cost per frame × frame count
Step 1: Check the scene fits one GPU
Open the Feedback Display and watch geometry and texture memory (Maxon: Memory). If the scene does not fit a 24 GB card, use 32 GB RTX 5090 GPUs.
Step 2: Time a frame on 2 GPUs
Render 2 or 3 representative frames and note the seconds per frame.
Step 3: Time the same frames on 4 or 8 GPUs
Compare seconds per frame and cost per frame. If doubling GPUs gives far less than double speed, per-frame overhead is high.
Step 4: Try several frames at once
For long, light sequences, render frame ranges in parallel on subsets of GPUs or on several servers.
Step 5: Plan the full job
Multiply the best cost per frame by your frame count. Our cost guide shows the calculation.
Quick check: if 8 GPUs are barely faster than 4 on your scene, split the frame range across two 4-GPU servers instead; per-frame preparation is likely dominating.
4. How fast are real Redshift jobs on multi-GPU servers?
Recorded iRender jobs: 150 frames rendered in 1h 42m on 4x RTX 5090 (40.84 s per frame), and 360 frames in 1h 18m on 8x RTX 4090. They are different scenes, so they show real job times rather than a scaling curve. Full results are in our Redshift benchmark.
Recorded Redshift jobs on iRender multi-GPU servers.
5. Multi-GPU server or a render farm that splits frames: which fits?
A SaaS render farm usually spreads frames across many machines, so turnaround for a finished scene can be short, and you do not manage hardware. An IaaS render farm such as iRender gives you a whole multi-GPU server: every GPU works on each frame, you can test frames first, and you can rent several servers to split a long sequence. Choose by what the job needs: final scene and shortest turnaround, or testing and control on the render hardware.
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. Extra render nodes need their own Redshift license according to Maxon, so check licensing before splitting a job across machines. Real render times are in our Redshift benchmark, and plans are on the Cinema 4D Redshift render farm page.
6. Redshift multi-GPU FAQ
Does Redshift use multiple GPUs?
Yes. Redshift can use all GPUs in a machine to render each frame, splitting it into buckets. It can also render several frames at once, each on a subset of GPUs.
Does Redshift combine VRAM from multiple GPUs?
No. According to Maxon support, Redshift does not combine VRAM when using multiple GPUs; each GPU can only use its own memory. Data that does not fit is handled out-of-core, which is slower.
Is 8 GPUs twice as fast as 4 GPUs in Redshift?
Not always. Maxon notes that rendering a single frame with all GPUs can give a non-linear gain, and the per-frame scene preparation does not speed up with more GPUs. Test your own scene on both and compare seconds per frame.
Should I render one frame on all GPUs or several frames at once?
Heavy frames usually benefit from all GPUs per frame. Long animations of light frames, where scene preparation is a large share of each frame, can finish sooner by rendering several frames at once on subsets of GPUs.
Can I mix an RTX 5090 and an RTX 4090 for Redshift?
Redshift can use both, but each GPU must hold the scene, so the smaller card’s 24 GB sets the in-VRAM limit for scenes rendered on both.
Do I need a separate Redshift license for each machine?
According to Maxon, the Redshift GPU license in a Cinema 4D subscription covers your main workstation; extra render nodes need their own Redshift license to render on the GPU.
