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Dispersed is a distributed GPU compute network. It is the RENDER NETWORK’s compute subnet focused on machine learning workloads (model inference and training) and other compute beyond the specialized task of rendering images. The GPUs in the network stay productive by distributing data processing tasks securely across GPUs on a global scale ensuring that your GPUs are profitable even if there are no 3D images or movies to render. The Dispersed network allows the collective power of enrolled GPUs to be used on general data processing and a wide range of diverse logic making them useful not only for parallel tasks. If you are a Service Consumer, you can rent powerful GPU’s to perform heavy computational tasks at an hourly rate.
  • Run heavy computational tasks such as model training, machine learning, analyzing large data sets, simulations etc.
  • No need to buy expensive hardware upfront. Rent, Execute, Release!

What is the purpose of Dispersed?

Dispersed is designed to democratise access to high-performance computing or grouped computing power. By replacing traditional, top-down control with the circular economy of a decentralised model, it distributes value among all network participants who carry a role. At its core, the platform allows consumers to scale indefinitely, allocating as many nodes as needed to tackle tasks far beyond the reach of standard consumer machines, and meanwhile allows providers to earn credits for contributing their spare computing power. By offering powerful GPUs at a fraction of the cost through hourly rentals, Dispersed provides an affordable, on-demand solution for heavy computational workloads, massive data crunching, and specialised use-cases that would otherwise be too slow or expensive to run locally.

Who can use Dispersed?

From individuals and hobbyists to researchers, scientists, developers and global enterprise users, everyone can leverage powerful GPUs to bridge the gap between high-level ambition and limited local hardware. Dispersed is for anyone whose projects and ideas need a scalable solution to level up.
  • Software Engineers / Researchers for model training and/or machine learning.
  • Data Scientists for crunching large amounts of data (Analyzing large data sets).
  • Game Developers for running heavy workloads like NPC/Physics simulations.
  • Engineers for running math-heavy simulated models.

Real world application

TaskWhyBenefit
Model trainingTrain large modelsFaster than personal computers.
No upfront hardware.
Image generationGenerate thousands of
images quickly.
Able to scale quickly, no upfront
hardware.
Big data data analysisCrunch massive datasets quicklyFast, no upfront hardware.
Scientific / Engineering simulationsRun math-heavy models for
Physics/Chemistry
Efficient, scalable, no upfront hardware, fast.
Stress testingRun heavy compute tasksAble to scale, no need to
maintain your own CPU cluster,
fast, cost effective.

Real-World user stories

  1. Developer Chatbot Training “I need to train a chatbot for my startup, but my laptop can’t handle weeks of computation. I rent GPUs on Dispersed for a few hours, train the model, and save thousands of dollars compared to buying my own hardware.”
  2. Data Scientist – Data Analysis “I have a dataset of 50 million records I need to analyze. My PCs can’t handle it. I cook up a CPU-heavy instance on Dispersed, crunch the numbers overnight, and only pay for the time I use.”
  3. Artist – Image Generation “I want to use neural rendering with my concept art in order to generate thousands of unique, high-fidelity images for a game. Instead of buying a high-end GPU, I rent cloud GPUs on Dispersed for a day and generate all images super fast and cheap!”
  4. Engineer – Scientific Simulation “I’m running a fluid dynamics simulation for a design project. It would take my workstation days to run. Using rented GPUs on Dispersed, the simulation only runs for a couple of hours.”
  5. Game Developer – Stress Testing “I need to simulate 10,000 players on my multiplayer game server to test performance. I rent CPUs on Dispersed, run the stress test and save thousands of Dollars and time without having to buy expensive server hardware.”
  6. ML Researcher – Experimenting with Models “I want to try multiple ML model architectures on large datasets. I rent CPUs on Dispersed to test multiple models in parallel, paying only for the hours I use instead of buying hardware for each experiment.”