Pay for completed work
Developers pay for accepted AI outputs, not unmanaged GPU hours.
The Decentralized Compute Company
NEW Building the verified execution layer for open AIWe are building a serverless AI execution network powered by user-owned GPUs.
Developers submit inference, evaluation, model-hosting, and fine-tuning workloads through a simple API. The network routes them to capable machines, verifies completed work, and pays hosts for accepted outputs.
Mission
AI infrastructure should be open, verifiable, and broadly owned.
AI capabilities are advancing quickly, but the infrastructure required to run them remains concentrated in a small number of datacenters. Building new capacity is capital intensive, slow, and constrained by power, cooling, procurement, networking, and regulation.
At the same time, millions of capable GPUs already exist in gaming PCs, workstations, laboratories, studios, and independent datacenters. TDCC coordinates this existing capacity into a trusted execution layer for open models and AI applications.
Make hardware productive
GPU owners earn when their machines complete useful, approved jobs.
Verify every job
Reputation, audits, and workload-specific checks improve trust.
Support open AI
Serve, evaluate, fine-tune, and host open-weight models and adapters.
The network
One API. Heterogeneous infrastructure underneath.
TDCC does not pretend that every GPU is identical. The scheduler understands model compatibility, VRAM, runtime support, latency, reliability, price, and reputation before assigning a workload.
01
Submit
A developer or AI agent submits a supported workload through the TDCC API.
02
Route
The scheduler selects a compatible host or certified micro-cluster.
03
Execute and verify
Approved runtimes execute the job. The network checks the result and records performance history.
04
Settle
Accepted work is settled automatically and the host earns for the useful compute delivered.
We do not rent GPUs. We sell verified AI work.
Initial workloads
Start with AI work that maps well to distributed hardware.
The initial network focuses on workloads that can be routed safely and efficiently across heterogeneous machines. Larger real-time models can be assigned to capable nodes or certified low-latency clusters.
Batch inferenceImage generationAudio transcriptionEmbeddingsEvaluationsSynthetic dataAgent workloadsLoRA / QLoRA fine-tuningOpen-model hostingAdapter serving
Research and engineering
Distributed systems, inference, and security belong together.
A permissionless compute network must tolerate unreliable machines, malicious behavior, model mismatches, inconsistent performance, and disputes over completed work.
TDCC combines distributed scheduling, secure runtimes, model-aware routing, proof-of-service, reputation, and programmable settlement into one execution layer.
FAQ
Clear answers for developers and GPU hosts.
Is TDCC another raw GPU marketplace?
No. TDCC is designed around completed AI work. Developers interact with a conventional API while routing, verification, reputation, failover, and settlement happen underneath.
Does TDCC combine random GPUs into one giant GPU?
No. Independent jobs are routed to compatible nodes. Workloads that require tightly coupled multi-GPU execution are assigned only to capable hosts or certified low-latency micro-clusters.
Why use decentralized infrastructure?
It can bring existing capacity online faster, broaden ownership of AI infrastructure, improve geographic diversity, and create a direct earning path for GPU owners.
What can GPU hosts earn from?
Hosts earn for accepted workloads completed on their hardware. Earnings depend on hardware capability, reliability, workload demand, energy cost, and network pricing.
Join the early network
Contribute compute or build on TDCC.
We are opening early conversations with GPU hosts, open-model teams, AI infrastructure developers, researchers, and pilot customers.