CreateOS and Fluence are partnering to bring decentralised, enterprise-grade GPU compute directly into the CreateOS workspace. For builders working with AI — running inference, fine-tuning models, deploying agents, or serving predictions — GPU compute sourced from Fluence's global network of professional data centres is now accessible without procurement friction, cloud vendor lock-in, or minimum spend commitments. With affordable, powerful GPUs now directly accessible on the platform, the future of MicroGPT deployments is within reach — smaller, specialised models fine-tuned for specific tasks, deployed on dedicated instances, at price points that work for independent builders. This is the infrastructure layer that makes the next generation of AI-native applications practical, not just for teams with six-figure cloud budgets.
GPU access has been one of the last stubborn bottlenecks in the builder workflow. The models are open. The frameworks are mature. The demand is real. But getting from "I want to run this model" to actually running it has meant navigating cloud provider dashboards, fighting for quota allocations, committing to contracts designed for enterprise procurement cycles, and paying rates that assume you have a DevOps team managing utilisation. For solo founders, small teams, and early-stage projects, the hardware cost alone has been enough to kill momentum before a prototype reaches production.
Fluence exists to restructure that equation from the supply side.
Fluence is a decentralised cloud platform built on open-source infrastructure and crypto-economic incentives, designed to deliver compute at up to 80% lower cost than the hyperscaler model. Rather than concentrating capacity in a handful of proprietary data centres controlled by a single vendor, Fluence aggregates GPU and CPU compute from a global network of verified, professional data centre operators — including Tier III and Tier IV facilities with GDPR, SOC2 and ISO27001 compliances. The hardware is real, the providers are vetted, and the infrastructure meets the reliability standards that production workloads demand.
What makes Fluence's architecture distinctive is how it treats compute. Physical servers and GPU deployments are orchestrated through the console platform, aligning provider incentives with service quality and creating a transparent marketplace where capacity scales with global supply rather than a single vendor’s infrastructure cycle.
The platform supports GPU containers, virtual machines, and bare metal configurations, with deployment accessible through the Fluence Console and scalable via the Fluence API. Fluence's AI roadmap extends into orchestration frameworks like LangChain, agentic stacks, and MCP servers, alongside composable data integrations with decentralised storage networks including Filecoin, Arweave, and IPFS. Their network has already generated over \(4 million in cloud savings for customers compared to traditional providers, and crossed \)1M in total network revenue in December 2025.
With this partnership, Fluence comes on board as the GPU compute partner for CreateOS. In practice, this means builders on the platform gain direct access to Fluence's decentralised GPU network from within the same workspace where they are already designing, deploying, and managing their applications. The compute layer is no longer a separate procurement relationship — it is part of the environment.
Rather than leaving CreateOS to provision GPU instances through a separate cloud provider, configure credentials, manage billing relationships, and wire everything together manually, the GPU compute is integrated into the deployment flow. The infrastructure is sourced from Fluence's network of professional data centres — enterprise-grade hardware operated by verified providers with uptime and reliability standards that match what production AI workloads require. The decentralised sourcing model means supply scales globally without the capacity constraints and regional bottlenecks that characterise traditional cloud GPU availability, and without the vendor lock-in that comes with committing to a single hyperscaler.
For builders on CreateOS, this partnership unlocks several capabilities that were previously either inaccessible or prohibitively expensive:
GPU-accelerated inference at deployment time. Provision GPU compute as part of the deployment flow, not as a separate infrastructure task. Run inference endpoints, serve predictions, and power AI features from within the same workspace where the application is built.
Fine-tuning and model serving without infrastructure overhead. Spin up dedicated GPU instances for fine-tuning domain-specific models, then serve them as persistent endpoints — without managing CUDA drivers, container orchestration, or cloud-specific networking configurations.
MicroGPT deployments within practical reach. Builders can deploy smaller, specialised models — fine-tuned for specific tasks and running on dedicated GPU instances — at price points that make this practical for independent teams and early-stage projects. A founder who wants a custom inference endpoint for their SaaS product, a fine-tuned classification model for their specific domain, or a persistent AI agent with dedicated compute can do it without the cost overhead that has historically restricted this to well-resourced AI labs.
Elastic GPU scaling without commitment contracts. Access GPU compute on demand without minimum spend requirements, long-term contracts, or quota request processes. Scale compute up for training runs, scale down when workloads are idle, and pay for what gets used — not for what gets reserved.
Decentralised resilience and geographic flexibility. Workloads are distributed across a global network rather than concentrated in a single vendor's region. This provides both geographic flexibility for latency-sensitive deployments and architectural resilience against the single points of failure inherent in centralised cloud infrastructure.
These capabilities flow from the combination of two things that have not previously existed in the same environment: a workspace that handles the full application lifecycle through natural language deployment and automated orchestration, and a GPU compute layer that operates at decentralised economics with enterprise-grade reliability. The integration means a builder's workflow — from idea to model training to deployed, GPU-powered application — happens in one place.
For teams and developers already using Fluence for GPU compute, CreateOS adds the application layer that sits on top of the infrastructure. Fluence provides the compute; CreateOS provides the workspace where that compute gets used to build, deploy, and manage complete applications. A Fluence user who has been provisioning GPU instances for AI workloads now has a direct path from compute to production-ready product — deployment through natural language, automated orchestration, unified visibility across the full stack, and a workspace designed to absorb the operational complexity that typically sits between "my model works" and "my product is live."
The partnership is live. Head to createos.io to provision GPU compute through the Fluence integration and deploy your first AI-native application — no separate cloud account, no infrastructure team, no minimum commitment. To explore Fluence's network architecture and compute marketplace, visit fluence.network.
About NodeOps
NodeOps unifies decentralized compute, intelligent workflows, and transparent tokenomics through CreateOS: a single workspace where builders deploy, scale, and coordinate without friction.
The ecosystem operates through three integrated layers: the Power Layer (NodeOps Network) providing verifiable decentralized compute; the Creation Layer (CreateOS) serving as an end-to-end intelligent execution environment; and the Economic Layer ($NODE) translating real usage into transparent token burns and staking yield.
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