With that said, an example would be a solo artist making CGI in C4D for a client/hobby (this is a small subset of potential use cases, but most relatable today).
Typical workflow costs would look like: - Local Workstation ($4K w/ 2 high end nvda gpus) - Render 2000 frames at 4K resolution for final render (need a better figure here but let's call it $500) - Time spent on workflow rendering - 5 hours - Time spent on final rendering - 10 hours
In a decentralized render situation: - Local computer ($500 - in theory no discrete graphics necessary) - Render 2000 frames at 4K resolution ($100) - Time spent on workflow rendering - 1 hour - Time spent on final rendering - 2 hours
I'm not trying to handwave at the data here btw, I just have a day job and want to answer promptly. RNDR is far and away cheaper at the moment than local or render farm compute: https://twitter.com/rsquires/status/1466599124451487744
Here are the main reasons why it's cheaper: - Most decentralized GPU operators are "running at a loss" -- people who own GPUs don't expect a return so any dollars earned while they're idle is good with them. - No "datacenter tax". Server GPUs are objectively more expensive than the network's GPU's which bias towards consumer. - In terms of time, rendering is one of the most parallelizable workloads. 2000 frames rendered sequentially on a cloud instance is just slower than 2000 frames rendered in parallel on 50 cloud instances.
For QoS, because it's so much cheaper, redundancy is much more justifiable to add to the system.
On compatibility, an SDK would allow people to build their own products that leverage the compute of the network. That's not something there is a high incentive for centralized render farms to offer.
If you want to go deep I highly recommend recent podcast with CEO of OTOY and the two hosts of the Mograph podcast: https://www.youtube.com/watch?v=SRtOMF4JKd4