Fluence is building what centralized clouds cannot: an open, low cost and enterprise grade compute layer that is sovereign, transparent, and open to everyone.
2025 has started the way 2024 ended with cloud giants investing aggressively to dominate AI infrastructure. Microsoft is spending over $80 billion on new data centers, Google launched its AI Hypercomputer, Oracle is investing $25 billion into its Stargate AI clusters, and AWS is prioritizing AI-native services. Specialized players are scaling rapidly too. CoreWeave raised $1.5 billion in its March IPO and is worth over $70 billion currently.
As AI becomes critical infrastructure, access to compute power will be one of the defining battles of our era. While hyperscalers consolidate and centralize compute power by building exclusive data centers and vertically integrating silicon, networks like Fluence offer a radically different vision—a decentralized, open, and neutral platform for AI compute, tokenizing compute to meet AI’s exponential demand and having FLT as a RWA Tokenized compute asset.
Fluence is already collaborating with top decentralized infrastructure networks across AI (Spheron, Aethir, IO.net) and storage (Filecoin, Arweave, Akave, IPFS) on multiple initiatives, reinforcing its position as a neutral compute-data layer. To bring this vision to life, the roadmap for 2025–2026 focuses on the convergence of three key action areas:
1. Launching A Global GPU-Powered Compute Layer
Fluence will soon support GPU nodes across the globe, enabling compute providers to contribute AI-ready hardware to the network. This new GPU mesh will upgrade Fluence platform from CPU-based capacity into an additional AI-grade compute layer, designed for inference, fine-tuning, and model serving. Fluence will integrate container support for secure, portable GPU job execution. Containerization enables reliable ML workload serving and establishes critical infrastructure for future inference, fine-tuning, and agentic applications across the decentralized network.
Fluence will explore privacy-preserving inference through confidential computing for GPUs, keeping sensitive business or personal data private while helping reduce costs of AI inference. Using trusted execution environments (TEE) and encrypted memory, this R&D initiative enables sensitive workload processing while maintaining decentralization and supporting sovereign agent development.
Key Milestones:
- GPU node onboarding – Q3 2025
- GPU container runtime support live – Q4 2025
- Confidential GPU computing R&D track kickoff – Q4 2025
- Pilot confidential job execution – Q2 2026
2. Hosted AI Models And Unified Inference
Fluence will provide one-click deployment templates for popular open-source models including LLMs, orchestration frameworks like LangChain, agentic stacks, and MCP servers. The Fluence platform AI stack will be expanded with an integrated inference layer for hosted models and agents. This simplifies AI model deployment while leveraging community contributions and external development support.
- Model + orchestration templates live – Q4 2025
- Inference endpoints and routing infra live – Q2 2026
3. Enabling Verifiable, Community-Driven SLA
Fluence will introduce a new approach to network trust and resilience through Guardians—retail and institutional actors who verify compute availability. Rather than relying on closed dashboards, Guardians monitor infrastructure through decentralized telemetry and earn FLT rewards for enforcing service-level agreements (SLAs).
Guardians turn an enterprise-grade infrastructure network into something anyone can participate in—without needing to own hardware. The Guardian program is complemented by the Pointless Program, a gamified reputation system that rewards community contributions and leads to Guardian eligibility.
Key Milestones:
- Guardian first batch – Q3 2025
- Guardians full rollout and programmatic SLA – Q4 2025
4. Integrating AI Compute with a Composable Data Stack
AI is not just compute—it’s compute + data. Fluence is building deep integrations with decentralized storage networks like Filecoin, Arweave, Akave, and IPFS to provide developers with access to verifiable datasets alongside execution environments. These integrations will allow users to define jobs that access persistent, distributed data and run on GPU-backed nodes—turning Fluence into a full-stack AI backend that is orchestrated via FLT.
To support this, the network will offer composable templates and prebuilt SDK modules for connecting compute jobs with storage buckets or on-chain datasets. Developers building AI agents, LLM inference tools, or science applications will be able to treat Fluence like a modular AI pipeline—with open data, compute, and validation stitched together by protocol logic.
Key Milestones:
- Decentralized storage backups – Q1 2026
- Integrated dataset access for AI workloads – Q3 2026
From Cloudless Compute To Shared Intelligence
With a roadmap focused on GPU onboarding, verifiable execution, and seamless data access, Fluence is laying the foundation for the next era of AI—one that will not be controlled by a handful of hyperscalers, but powered by a global community of cooperating and decentralized compute providers and participants
The infrastructure for AI must reflect the values we want AI to serve: openness, collaboration, verifiability and accountability. Fluence is turning that principle into a protocol.
Join the mission:
- Apply as a GPU provider
- Sign up for the Fluence Beta for Cloudless VMs
Start climbing the Pointless leaderboard and earn your way to Guardian status
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