Render Network and Salad Bring AI Compute Payments Onchain, Expanding RENDER Across 60,000+ Daily GPUs
Salad’s distributed computing network, now operating across 180+ countries, settles node rewards and customer payments onchain, giving node operators greater flexibility in how they receive and redeem earnings as centralized GPU costs continue to rise.
Grand Cayman, Cayman Islands — April 7, 2026 — The Render Network Foundation, the governance entity behind the decentralized GPU computing network Render Network, announced today that Salad, a distributed cloud computing company, has selected Render Network as its exclusive onchain payments layer. The integration moves Salad network payments and node-operator rewards—estimated at more than $2.3M/year—onchain using RENDER tokens, creating a transparent, blockchain-based mechanism for distributing rewards and processing payments across one of the world’s largest distributed GPU networks.
The announcement comes at a critical moment for AI infrastructure. In January, AWS quietly raised the price of its H200 GPU instances by more than 15% with no advance notice, widening the cost gap for large-scale AI workloads and forcing developers and enterprises to explore alternatives. At the scale AI now demands, traditional cloud pricing and limited GPU availability are forcing developers and enterprises to rethink how infrastructure costs are managed and how operators are compensated.
Salad operates across 180+ countries with 60,000 active GPUs, powering AI inference, video rendering, scientific research, and other high-performance computing workloads. By introducing RENDER-based payments for node operators (known as “Chefs”), the integration expands how participants in the network are compensated, while also enabling customers to pay in RENDER alongside existing billing options.
“Shifting our payments and rewards onchain gives our Chefs more control over how they receive and use their earnings,” said Bob Miles, Salad’s Founder & CEO. “At a time when cloud costs are rising and GPU availability is constrained, this integration helps us keep compute accessible and scalable for the workloads ahead.”
For developers and enterprises, the integration adds a flexible payment layer to Salad’s existing large-scale compute offerings across AI, rendering, and research workloads. For node operators, it introduces the ability to withdraw onchain rewards to self-custodied wallets or redeem them through Salad’s storefront.
“By enabling Salad’s network to handle payments and rewards onchain, we’re opening a scalable path for real-world infrastructure to incorporate blockchain without overhauling their business,” said Tristan Relly, Head of Operations, Render Network Foundation. “This is how traditional distributed networks position to meet the speed and scale of the emerging AI economy.”
By moving node rewards and customer payments onchain, Salad introduces a model where distributed compute operators are paid faster, with more flexibility, and in a way that scales with the AI economy. This marks a step toward integrating blockchain-native finance directly into real-world infrastructure.
To learn more, visit https://blog.salad.com/salad-joins-render-network/.
About Salad
Salad operates globally distributed cloud infrastructure supporting a wide range of customers from AI Startups to Fortune 500 Enterprise organisations. Both datacenters and individuals can share latent computational resources with Salad, turning the world’s idle hardware into meaningful rewards. With thousands of existing customers running production workloads across tens of thousands of machines, Salad powers cutting-edge compute applications for far less than hyperscale providers.
About Render Network Foundation
The Render Network Foundation is the governance organization for the world’s leading decentralized compute network, the Render Network. The network connects node operators looking to monetize their idle GPU compute power with artists looking to scale intensive 3D-rendering work and with machine learning developers looking to train and tune AI models.
