How to choose components for 3DS Max or Maya?

Some FAQ’s that we get asked a lot about builds needing suitable configuration for Modeling and Animation performance in Maya and 3DS Max –

What is the requirement for the best animation and modeling performance in Maya and 3DS Max?
Animation & Modeling are both single thread dependent – what you should be looking for is the highest possible clock speed in your budget; However core count shouldn’t really be given extra emphasis (core count really matters in rendering – see next question)

Do these software require additional hardware to do rendering effectively ?
Both 3DS Max (from 2018) and Maya (since 2017) use “Arnold” as their default renderer – this is a CPU based rendering engine which benefits greatly from a higher number of cores – in CPU comparisons it is found that performance scales well with more number of cores. Therefore configuration with high CPU core count help in rendering faster through Arnold – HOWEVER – you will need a decent to good graphic card if you are looking to use different rendering engines like Furryball, V-Ray, Octane etc.

What about RAM?
How much RAM you need is decided by the complexity of your scene & the resolution – higher RAM leaves headroom for more complex scenes. Regular users will find that 16 to 32 GB of RAM to be more than sufficient, for those who need more 64 GB is also an option.

Core Counts ? So do  i need a Xeon processor ?
Not exactly – higher core counts mean lower clock speeds. So what you really need is a processor boosting to higher clock speeds when fewer cores are working (for modeling and animation ) AND still have decent clock speeds when more cores work together for rendering.

In previous generation CPU’s, the XEON line up was much more robust than their core series counterparts. HOWEVER in the current generation line ups, there is very little functional difference left between the CORE and XEON product families.  We would recommend i7 and i9 CPUs for a Maya and 3DS workstation with a balanced performance across animation, modeling and rendering. For some specific rendering engines you may still need dual Xeon builds.

Does a SSD help?
Yes! SSD will help loading times across the board – right from booting up windows and launching these software as well.

So – Quadro GPU or Geforce GPU works fine ?
Maya & 3DS Max need a decently fairly powerful video card, unless you are exclusively rendering only. Officially Autodesk sates that Quadro cards are certified for these applications – however they do test Geforce cards too – so these will work fine for most users. However Quadro cards are purpose build AND optimized by Nvidia to be more reliable in the long term.

To Summarize – 
CPU – High Clock Speed for modeling & animation and Higher core count for rendering

Graphic Cards – A decent graphic card (GTX 1060 6GB and above ) will let you see the viewport in 60 fps and above for a smoother performance – Quadro cards are certified with these software , so selecting Quadro if your budget allows is a good idea.

RAM – 16 GB and above is sufficient for most users – however the complexity of your use case may need 32 or even 64GB of RAM

SSD – Yes for the loading times! With a secondary drive for storage; SSD should be used for Windows and software installations



What is Machine Learning?

We hear a lot about Machine Learning & Deep Learning these days – that the next wave of jobs are going to be in AI and Machine learning – we attempt to answer some questions here and give some insight on component selection.

What is it?

Machine learning is focused on using computer algorithms which draw predictive insight from static or dynamic data sources – this is done using analytic and probabilistic models – refined using training and feedback.

It uses pattern recognition, artificial intelligence learning methods, and statistical data modeling. “Learning” happens in two ways – Supervised Learning ; when the desired outcome is known and an annotated data set used to train / fit a model to accurately predict values outside the training set. Unsupervised learning ; uses raw data to seek out inherent partitions or a group of characteristics present in the data itself.

Machine learning combines varied disciplines of computer science, mathematics, graph and network theory, statistics and probability – practically endless areas of application

A lot more computing can be accomplished with a workstation and a few graphic cards than what the whole planet’s computing power could do in 1990 ! Let that sink in. Estimated worldwide data storage is in zettabyte ranges , 1 zettabyte is a trillion gigabytes. We’re probably staring down the golden age of Machine Learning right now!

Right time ?

Humongous amounts of data from scientific instruments, business records, mature and well annotated databases – things look to be falling in place for ML. Inexpensive computing resources and established programming methods for utilizing GPU acceleration AND there is the money. Money in research, startups and big business budget allocations – Data Scientist is the hottest job title.

Quite understandably, the single biggest driving force for the rise of Machine Learning is to try and sell you stuff! High paying jobs in places like Google, Baidu, Microsoft & Facebook – some really amazing work is being done to improve marketing and advertising.

The applications ?

ML has huge potential for scientific discovery – terabytes of data generated by research instruments open up possibilities for discovery in chemistry, bio-medicine, economics etc. Great potential for robotics, autonomous vehicles, translation and voice recognition is a no-brainer.

To realize how far computing power has come along , here’s a short note for perspective. In 2012 Google set out analyzing YouTube data using a neural network with unsupervised learning – the feature extraction classifier discovered (on its own) that Cat videos are the most popular on YouTube. This work was accomplished using a conventional computing cluster for training – consisted of 1000 nodes with 1600 cores! Today you could run a model with the exact same complexity using 2 workstations with 4 GPU’s in each. This makes for a great use case for GPU acceleration in ML.

What configuration should i choose?

Short answer is – it depends. On your data set and your work load
Part selection guideline would be some thing like the below –

  • One or Two Nvidia Pascal GPU’s
  • Intel i7 or i9 CPU
  • 32GB to 62GB of system memory (RAM)

Recommended configuration –

  • GPUS – 2 nos. (GTX 1070, GTX 1080ti or Titan Xp) (Cuda Cores are your friends!)
  • Core i9 7900X (10 Core)
  • 64GB of Memory
  • 1 TB SSD/HDD (SSD is preferred for faster data transfer)

A more basic configuration can be arrived at, optimizing part selection based on budget and use case – not every application will need a i9 7900x – an i5 8400 can do the job for smaller data sets (although not as fast)

Do comment if you want to see a more detailed reasoning on component selection or shoot us an email at for any questions or queries.

Until next time. – High Performance Systems