For our clients who recurrently carry out rendering workloads reminiscent of animation or visible results studios, there’s a mounted period of time to ship a challenge. When confronted with a looming deadline, these clients can leverage cloud assets to quickly increase their fleet of render servers to assist full work inside a given timeframe, a course of often called burst rendering. To be taught extra about deploying rendering jobs to Google Cloud, see Building a Hybrid Render Farm.

When gauging render efficiency on the cloud, clients typically reproduce their on-premises render employee configurations by constructing a digital machine (VM) with the identical variety of CPU cores, processor frequency, reminiscence, and GPU. Whereas this can be a very good place to begin, the efficiency of a bodily render server isn’t equal to a VM working on a public cloud with an analogous configuration. To be taught extra about evaluating on-premises {hardware} to cloud assets, see the reference article Resource mappings from on-premises hardware to Google Cloud.

With the pliability of cloud, you possibly canright-size your assets to match your workload. You may outline every particular person useful resource to finish a process inside a sure time, or inside a sure finances. 

However as new CPU and GPU platforms are launched or costs change, this calculation can grow to be extra complicated. How will you inform in case your workload would profit from a brand new product obtainable on Google Cloud?

This text examines the efficiency of various rendering software program on Compute Engine cases. We ran benchmarks for fashionable rendering software program throughout all CPU and GPU platforms, throughout all machine sort configurations to find out the efficiency metrics of every. The render benchmarking software program we used is freely-available from a wide range of distributors. You may see a listing of the software program we used within the desk under, and be taught extra about every in Examining the benchmarks.

Be aware: Benchmarking of any render software program is inherently biased in direction of the scene information included with the software program and the settings chosen by the benchmark writer. You might wish to run benchmarks with your personal scene information inside your personal cloud setting to totally perceive methods to make the most of the pliability of cloud assets.

Benchmark overview

Render benchmark software program is often offered as a standalone executable containing every thing essential to run the benchmark: a license-free model of the rendering software program itself, the scene or scenes to render, and supporting information are all bundled in a single executable that may be run both interactively or from a command line.

Benchmarks might be helpful for figuring out the efficiency capabilities of your configuration when in comparison with different posted outcomes. Benchmarking software program reminiscent of Blender Benchmark use job length as their essential metric; the identical process is run for every benchmark irrespective of the configuration. The sooner the duty completes, the upper the configuration is rated.

Different benchmarking software program reminiscent of V-Ray Bench examines how a lot work might be accomplished throughout a hard and fast period of time. The quantity of computations accomplished by the top of this time interval offers the consumer with a benchmark rating that may be in comparison with different benchmarks.

Benchmarking software program is topic to the constraints or options of the renderer on which they’re primarily based. For instance, software program reminiscent of Octane or Redshift can not make the most of CPU-only configurations as they’re each GPU-native renderers. V-Ray from ChaosGroup can make the most of each CPU and GPU however performs completely different benchmarks relying on the accelerator, and subsequently can’t be in contrast to one another.

We examined the next render benchmarks:

Selecting occasion configurations

An occasion on Google Cloud might be made up of just about any mixture of CPU, GPU, RAM, and disk. With a purpose to gauge efficiency throughout a lot of variables, we outlined methods to use every part and locked its worth when essential for consistency. For instance, we let the machine sort decide how a lot reminiscence was assigned to every VM, and we created every machine with a 10 GB boot disk.

Quantity and sort of CPU

Google Cloud affords quite a few CPU platforms from completely different producers. Every platform (known as Machine Sort within the Console and documentation) affords a variety of choices, from a single vCPU all the best way as much as the m2-megamem-416. Some platforms supply completely different generations of CPUs, and new generations are launched on Google Cloud as they arrive available on the market.

We restricted our analysis to predefined machine varieties on N1, N2, N2D, E2, C2, M1, and M2 CPU platforms. All benchmarks have been run on a minimal of Four vCPUs, utilizing the default quantity of reminiscence allotted to every predefined machine sort.

Quantity and sort of GPU

For GPU-accelerated renderers, we ran benchmarks throughout all combos of all NVIDIA GPUs obtainable on Google Cloud. To simplify GPU renderer benchmarks, we used solely a single, predefined machine sort, the n1-standard-8, as most GPU renderers do not make the most of CPUs for rendering (excluding V-Ray’s Hybrid Rendering characteristic, which we did not benchmark for this text).

Not all GPUs have the identical capabilities: some GPUs assist NVIDIA’s RTX, which might speed up sure raytracing operations for some GPU renderers. Different GPUs supply NVLink, which helps sooner GPU-to-GPU bandwidth and affords a unified reminiscence area throughout all hooked up GPUs. The rendering software program we examined works throughout all GPU varieties, and is ready to leverage these kind of distinctive options, if obtainable.

For all GPU cases we put in NVIDIA driver model 460.32.03, obtainable from NVIDIA’s public obtain driver page in addition to from our public cloud bucket. This driver runs CUDA Toolkit 11.2, and helps options of the brand new Ampere structure of the A100’s.

Be aware: Not all GPU varieties can be found in all areas. To view obtainable areas and zones for GPUs on Compute Engine, see GPUs regions and zone availability.

Sort and measurement of boot disk

All render benchmark software program we used takes up lower than a number of GB of disk, so we stored the boot disk for every take a look at occasion as small as doable. To attenuate price, we selected a boot disk measurement of 10 GB for all VMs. A disk of this measurement will solely ship modest efficiency, however rendering software program sometimes ingest scene information into reminiscence previous to working the benchmark; disk I/O has little impact on the benchmark.


All benchmarks have been run within the us-central1 area. We positioned cases in numerous zones throughout the area, primarily based on useful resource availability. 

Be aware: Not all useful resource varieties can be found in all areas. To view obtainable areas and zones for CPUs on Compute Engine, see available regions and zones. To view obtainable areas and zones for GPUs on Compute Engine, see GPUs regions and zone availability.

Calculating benchmark prices

All costs on this article are calculated inclusive of all occasion assets (CPU, GPU, reminiscence, and disk) for less than the length of the benchmark itself. Every occasion incurs startup time, driver and software program set up, and latency previous to shutdown following the benchmark. We did not add this further time to the prices proven, which might be decreased by baking a picture or by working inside a container. 

Costs are present on the time of writing, primarily based on assets within the us-central1 area, and are in USD. All costs are for on-demand assets; most rendering clients will wish to use preemptible VMs, that are well-suited for rendering workloads, however for the needs of this text it is extra vital to see the relative variations between assets than total price. See the Google Cloud Pricing Calculator for extra particulars.

To give you hourly prices for every machine sort, we added collectively the varied assets that make up every configuration:

price/hr = vCPUs + RAM (GB) + boot disk (GB) + GPU (if any)

To get the price of a person benchmark, we multiplied the length of the render by this price/hr:

complete price = price/hr * render length

Price efficiency index

Calculating price primarily based on how lengthy a render takes solely works for benchmarks that use render length as a metric. Different benchmarks reminiscent of V-Ray and Octane calculate a rating by measuring the quantity of computations doable inside a hard and fast time period. For these benchmarks, we calculate the Cost Performance Index (CPI) of every render, which might be expressed as:

CPI = Worth / Price

For our functions, we substitute Worth with Rating, and Price with the hourly price of the assets:

CPI = rating / price/hr

This offers us a single metric that represents each worth and the efficiency of every occasion configuration.

Calculating CPI on this method makes it simple to match outcomes to one another inside a single renderer; the ensuing values themselves aren’t as vital as how they examine to different configurations working the identical benchmark. 

For instance, look at the CPI of three completely different configurations rendering the V-Ray Benchmark:

two 2.png

To make these values simpler to understand, we will normalize them by defining a pivot level; a goal useful resource configuration that has a CPI of 1.0. On this instance, we use n1-standard-Eight as our goal useful resource:


This makes it simpler to see that the n2d-standard-Eight has a CPI that is round 70% increased than that of the n1-standard-8.

For CPU benchmarks, we outlined the goal useful resource as an n1-standard-8. For GPU benchmarks, we outlined the goal useful resource as an n1-standard-Eight with a single NVIDIA P100. A CPI higher than 1.Zero signifies higher price/efficiency in comparison with the goal useful resource, and CPI lower than 1.Zero signifies decrease price/efficiency in comparison with the goal useful resource.

For formulation for calculating CPI utilizing the goal useful resource might be expressed as:

CPI = (rating / price/hr) / (target-score / target-cost/hr)

We use CPI within the Examining the benchmarks part.

Evaluating occasion configurations

Our first benchmark examines the efficiency variations between quite a few predefined N1 machine sort configurations. Once we run the Blender Benchmark on a choice of six configurations and examine length and the price to carry out the benchmark (price/hr x length), we see an attention-grabbing outcome:


The fee for every of those benchmarks is nearly equivalent, however the length is dramatically completely different. This tells us that the Blender renderer scales nicely as we enhance the variety of CPU assets. For a Blender render, if you wish to get your outcomes again rapidly, it is sensible to decide on a configuration with extra vCPUs.

Once we examine the N1 CPU platform to different CPU platforms, we be taught much more about Blender’s rendering software program. Examine the Blender Benchmark throughout all CPU platforms with 16 vCPUs:


The graph above is sorted based on price, with least costly on the appropriate. The N2D CPU platform (which makes use of AMD EPYC Rome CPUs) is the bottom price and completes the benchmark within the shortest period of time. This may increasingly point out that Blender can render extra effectively on AMD CPUs, a indisputable fact that can be noticed on their public benchmark results page. The C2 CPU platform (which makes use of Intel Cascade Lake CPUs) is available in a detailed second, presumably as a result of it affords the best sustained frequency of three.9 GHz.

Be aware: Whereas a couple of pennies’ distinction could seem trivial for a single render take a look at, a typical animated characteristic is 90 minutes (5400 seconds) in length. At 24 frames per second, that is roughly 130,000 frames to be rendered for a single iteration. Some parts can undergo tens and even tons of of iterations earlier than closing approval. A miniscule distinction at this scale can imply an enormous distinction in price by the top of a manufacturing.


Blender Benchmark lets you examine CPU and GPU efficiency utilizing the identical scenes and metrics. The benefit of GPU rendering is revealed after we examine the earlier CPU outcomes to that of a single NVIDIA T4 GPU:


The Blender Benchmark is each sooner and cheaper when run in GPU mode on an n1-standard-Eight with a single NVIDIA T4 GPU hooked up. Once we run the benchmark on all GPU varieties, the outcomes can fluctuate extensively in each price and length:


GPU efficiency

Some GPU configurations have a better hourly price, however their efficiency specs give them a greater cost-to-performance benefit than lower-cost assets.

For instance, the FP64 efficiency of the NVIDIA Tesla A100 (9.7 TFLOPS) is 38 instances increasedthan that of the T4 (0.25 TFLOPS), but the A100 is round 9 instances the price. Within the above diagram, the P100, V100, and A100 price nearly the identical, but the A100 completed the render nearly twice as quick because the P100.

By far essentially the most cost-effective GPU within the fleet is the NVIDIA T4, but it surely did not outperform the P100, V100, or A100 for this specific benchmark.

All GPU benchmarks (besides the A100, which used the a2-highgpu-1g configuration) used the n1-standard-Eight configuration with a 10 GB PD-SSD boot disk:


We will additionally look at how the identical benchmark performs on an occasion with multiple GPU hooked up:


The NVIDIA V100-Eight configuration might full the benchmark quickest, but it surely additionally incurs the best price. The GPU configuration with the best worth seems to be 2x NVIDIA T4 GPUs, which full the work quick sufficient to price much less than the 1x NVIDIA T4 GPU.

Lastly, we examine all CPU and GPU configurations. The Blender Benchmark returns a length, not a rating, so we will use the price of every benchmark to signify CPI. Within the graph under, we use the n1-standard-8 (with a CPI of 1.0) as our goal useful resource, to which we examine all different configurations:

Ten 10.png

This confirms that the best worth configuration to run the Blender Benchmark is the 2x NVIDIA T4 GPU configuration working the benchmark in GPU mode.

Diminishing returns

Rendering on a number of GPUs might be more cost effective than on a single GPU. The efficiency enhance some renderers can acquire from a number of GPUs can exceed that of the price enhance, which is linear.

The efficiency positive factors begin to diminish as we add a number of V100s, subsequently the worth can be diminished while you issue within the elevated price. This noticed flattening of the efficiency curve is an instance of Amdahl’s Law. Including assets to scale efficiency can lead to a efficiency enhance, however solely up to some extent, after which you are inclined to expertise diminishing returns in efficiency. Many renderers will not be able to 100% parallelization, and subsequently can not scale linearly as assets are added.

As with GPU assets, the identical might be noticed throughout CPU assets. On this diagram, we observe how benchmark efficiency positive factors diminish because the variety of N2D vCPUs climbs:


The above diagram exhibits that efficiency positive factors begin to diminish above 64 vCPUs the place the price, surprisingly, drops a bit earlier than climbing once more.

Working the benchmarks

To make sure correct, repeatable outcomes, we constructed a easy, programmatic, reproducible testing framework that makes use of easy elements of Google Cloud. We might even have used a longtime benchmarking framework reminiscent of PerfKit Benchmarker

To look at the uncooked efficiency of every configuration, we ran every benchmark on a brand new occasion working Ubuntu 1804. We ran every benchmark configuration six instances in a row, discarding the primary cross to account for native disk caching or asset load, and averaged the outcomes of the remaining passes. This technique, in fact, does not essentially replicate the truth of a manufacturing setting the place issues like community visitors, queue administration load, and asset synchronization might have to be considered.

Our benchmark workflow resembled the next diagram:


Analyzing the benchmarks

The renderers we benchmarked all have distinctive qualities, options, and limitations. Benchmark outcomes revealed some attention-grabbing information, a few of which is exclusive to a specific renderer or configuration, and a few of which we discovered to be frequent throughout all rendering software program.

Blender benchmark

Blender Benchmark was essentially the most extensively examined of the benchmarks we ran. Blender’s renderer (known as Cycles) is the one renderer in our checks that is ready to run the identical benchmark on each CPU and GPU configurations, permitting us to match the efficiency of utterly completely different architectures.

Blender Benchmark is freely obtainable and is open supply so you possibly can even modify the code to incorporate your personal settings or render scenes.

The Blender Benchmark contains quite a few completely different scenes to render. All our Blender benchmarks rendered the next scenes:

  • bmw27
  • classroom
  • fishy_cat
  • koro
  • pavillon_barcelona

You may be taught extra concerning the above scenes on the Blender Demo Files web page.

Download Blender Benchmark (model 2.90 used for this text)
Blender Benchmark documentation
Blender Benchmark public results

Benchmark observations

Blender Cycles seems to carry out in a constant trend as assets are elevated throughout all CPU and GPU configurations, though some configurations are topic to diminishing returns, as famous earlier:


Subsequent, we look at price. With a number of exceptions, all benchmarks price between $0.40 and $0.60, irrespective of what number of vCPUs or GPUs have been used:


This can be extra of a testomony to how Google Cloud designed its useful resource price mannequin, but it surely’s attention-grabbing to notice that every benchmark carried out the very same quantity of labor and generated the very same output. Investigating the design of Blender Cycles and the way it manages useful resource utilization is past the scope of this text, nevertheless the source code is freely obtainable for anybody to see, ought to they be enthusiastic about studying extra.

The CPI of Blender is the inverse of the benchmark price, however evaluating it to our goal useful resource (the n1-standard-8) reveals the best worth configurations to be any mixture of T4 GPUs. The bottom worth assets are the M2 machine varieties, because of their price premium and the diminishing efficiency returns we see within the bigger vCPU configurations:


V-Ray benchmark

V-Ray is a versatile renderer by ChaosGroup that’s appropriate with many 2D and 3D purposes, in addition to actual time sport engines.

V-Ray Benchmark is on the market as a standalone product free of charge (account registration required) and runs on Home windows, Mac OS, and Linux. V-Ray can render in CPU and GPU modes, and even has a hybrid mode the place it makes use of each.

V-Ray might run on each CPU and GPU, however their benchmarking software program renders completely different pattern scenes, and makes use of completely different models to match outcomes on every platform (CPU makes use of vsamples, GPU makes use of vpaths). We have now grouped our V-Ray benchmark outcomes into separate CPU and GPU configurations.

Download V-Ray Benchmark (model 5.00.01 used for this text)
V-Ray Bench documentation
V-Ray Bench public results

Benchmark observations

For CPU renders (utilizing mode=vray for the benchmark), V-Ray seems to scale nicely because the variety of vCPUs will increase, and might take good benefit of the extra fashionable CPU architectures provided on GCP, notably the AMD EPYC within the N2D and the Intel Cascade Lake within the M2 Ultramem machine varieties:


Wanting on the CPI outcomes, there seems to be a candy spot the place you get essentially the most worth out of V-Ray, someplace between Eight and 64 vCPUs. Scores for Four vCPU configurations all are usually decrease than the typical of every machine sort, and the bigger configurations begin to see diminishing returns because the vCPU rely climbs.

The M1 and M2 Ultramem configurations are nicely under the CPI of our goal useful resource (the n1-standard-8) as they’ve a price premium that offsets their spectacular efficiency. If in case you have the finances, nevertheless, you’ll get one of the best uncooked efficiency out of those machine varieties.

One of the best worth seems to be from the N2D-standard-8, in case your workload can match into 32 GB of RAM:


In GPU mode (utilizing mode=vray-gpu-cuda), V-Ray helps a number of GPUs nicely, scaling in a near-linear trend with the variety of GPUs.

It additionally seems that V-Ray is ready to take good benefit of the brand new Ampere structure on the A100 GPUs, exhibiting a 30-35% enhance in efficiency over the V100:


This boosted efficiency comes at a price, nevertheless. The CPI for the 1x and 2xA100 configurations are solely barely higher than the goal useful resource (1xP100), and the 4x, 8x, and 16x configurations get more and more costly in comparison with efficiency capabilities. 

As with all the opposite benchmarks, all configurations of the T4 GPU revealed the best worth GPU within the fleet:


Octane bench

Octane Render by OTOY is an unbiased, GPU-only renderer that’s built-in with hottest 2D, 3D, and sport engine purposes.

Octane Bench is freely obtainable for obtain and returns a rating primarily based on the efficiency of your configuration. Scores are measured in Ms/s (mega samples per second), and are relative to the efficiency of OTOY’s chosen baseline GPU, the NVIDIA GTX 980. See Octane Bench’s results page for extra data on how the Octane Bench rating is calculated.

Download Octane Bench (model 2020.1.Four used for this text)
Octane Bench documentation
Octane Bench public results

Benchmark observations

Octane Render scores comparatively excessive throughout most GPUs provided on GCP, particularly the a2-megagpu-16g machine sort, which took the highest rating of their outcomes when first publicly announced:


All configurations of the T4 delivered essentially the most worth, however P100’s and A100’s scored above the goal useful resource. Curiously, including a number of GPUs improved the CPI in all circumstances, which isn’t at all times the case with the opposite benchmarks:

twenty 1.png

Redshift render

Redshift Render is a GPU-accelerated, biased renderer by Maxon, and integrates with 3D purposes reminiscent of Maya, 3DS Max, Cinema 4D, Houdini, and Katana.

Redshift features a benchmarking software as a part of the set up, and the demo model doesn’t require a license to run the benchmark. To entry the assets under, join a free account here.

Download Redshift (model 3.0.31 used for this text)
Redshift Benchmark documentation
Redshift Benchmark public results

Benchmark observations

Redshift Render seems to scale in a linear method because the variety of GPUs is elevated:


When benchmarking on the NVIDIA A100 GPUs, we begin to see some limitations. Each the 8xA100 and 16xA100 configurations ship the identical outcomes, and are solely marginally sooner than the 4xA100 configuration. Such a quick benchmark could also be pushing the boundaries of the software program itself, or could also be restricted by different components such because the write efficiency of the hooked up persistent disk:

twenty 3.png

The NVIDIA T4 GPUs have the best CPI by far, because of their low price and aggressive compute efficiency, notably when a number of GPUs are used. Sadly, the constraints famous within the 8x and 16xA100 GPUs lead to a decrease CPI, however this might be as a result of limits of this benchmark structure and instance scene.


This information can assist clients who run rendering workloads resolve which assets to make use of primarily based on their particular person job necessities, finances, and deadline. Some easy takeaways from this analysis:

When you aren’t time-constrained, and your render jobs do not require numerous reminiscence, you could wish to select smaller, preemptible configurations with increased CPI, such because the N2D or E2 machine varieties.

When you’re below a deadline and fewer involved about price, the M1 or M2 machine varieties (for CPU) or A2 machine varieties (for GPU) can ship the best efficiency, however might not be obtainable as preemptible or might not be obtainable in your chosen area.


We hope this analysis helps you higher perceive the traits of every compute platform and the way efficiency and price might be associated for compute workloads.

Listed here are some closing observations from all of the render benchmarks we ran:

  • For CPU renders, N2D machine varieties seem to offer one of the best efficiency at an affordable price, with the best flexibility (as much as 224 vCPUs on a single VM). 
  • For GPU renders, the NVIDIA T4 delivers essentially the most worth because of its low worth and Turing structure, which is able to working each RTX and TensorFlow workloads. You might not have the ability to run some bigger jobs on the T4 nevertheless, as every GPU is proscribed to 16 GB of reminiscence. When you want extra GPU reminiscence, you could wish to have a look at a GPU type that provides NVLink, which unifies the reminiscence of all hooked up GPUs.
  • For sheer horsepower, the M2 machine varieties supply large core counts (as much as 416 vCPUs working at 4.Zero GHz) with an astounding quantity of reminiscence (as much as 11.7 GB). This can be overkill for many jobs, however a fluid simulation in Houdini or a 16ok architectural render may have the additional assets to efficiently full. 
  • If you’re in a deadline crunch or want to deal with last-minute adjustments, you should use the CPI of varied configurations that can assist you price mannequin manufacturing workloads. When mixed with efficiency metrics, you possibly can precisely estimate how a lot a job ought to price, how lengthy it would take, and the way nicely it would scale on a given structure.
  • The A100 GPUs within the A2 machine sort supply large positive factors over earlier NVIDIA GPU generations, however we weren’t in a position to run all benchmarks on all configurations. The Ampere platform was comparatively new after we ran our checks, and assist for Ampere hadn’t been launched for all GPU-capable rendering software program.

Some clients select assets primarily based on the calls for of their job, no matter worth. For instance, a GPU render might require an unusually excessive quantity of texture reminiscence, and will solely efficiently full on a GPU sort that provides NVLink. In one other situation, a render job might should be delivered in a brief period of time, no matter price. Each of those eventualities might steer the consumer in direction of the configuration that may get the job carried out, fairly than the one with the best CPI.

No two rendering workloads are the identical, and no single benchmark can present the true compute necessities for any job. You might wish to run your own proof-of-concept render test to gauge how your personal software program, plugins, settings, and scene information carry out on cloud compute assets.

Different benchmarking assets

Keep in mind we did not benchmark different metrics reminiscent of disk, reminiscence, or community efficiency. See the next articles for extra data, or to learn to run your personal benchmarks on Google Cloud:

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