AI companies
Get compute for training, fine-tuning, inference, and evaluation.
Version 1.0 · July 2026
The compute layer for AI.
TDCC connects AI companies with data centers and GPUs so they can train, fine-tune, and run models without managing every machine themselves.
01 / The problem
AI needs more GPUs than most teams can access.
AI companies need compute for training, fine-tuning, and inference. Data centers and GPU operators have capacity, but connecting the right job to the right machine is still difficult.
TDCC makes that connection simple: submit a job, get it run, and pay for an accepted result.
02 / Mission
Make the full AI model lifecycle easier.
Data centers
Turn available GPU capacity into useful, recurring work.
GPU operators
Earn by running approved AI jobs on compatible hardware.
Developers
Use one API instead of building your own compute network.
03 / How it works
One API. The right hardware underneath.
01
Connect capacity
Data centers and GPU operators share hardware, availability, and pricing.
02
Submit a job
An AI team sends a training, fine-tuning, inference, or evaluation job.
03
Run it
TDCC matches the job with hardware that fits.
04
Verify it
We check the result and pay hosts for accepted work.
Simple promise: AI teams bring the models. TDCC brings the compute.
04 / Product
Everything AI teams need to run models.
Training
Train models on suitable distributed capacity.
Fine-tuning
Customize open models with LoRA and QLoRA.
Inference
Run batch jobs, APIs, image generation, and embeddings.
Evaluation
Test models, generate data, and compare results.
05 / Roadmap
A practical path from coordination to network scale.
Phase I · FoundationDefine workload schemas, host requirements, secure runtimes, scheduling primitives, and verification policies.
Phase II · Pilot networkConnect selected AI companies and data centers. Validate training, fine-tuning, inference, and evaluation workflows with measurable service levels.
Phase III · Capacity expansionOnboard additional data centers and qualified GPU operators, improve routing, introduce reputation signals, and expand geographic resilience.
Phase IV · Production platformOffer a stable developer API, observability, billing, automated settlement, and enterprise-grade controls for sustained AI workloads.
06 / Capital and momentum
Build the network with focused, accountable capital.
TDCC raised $50,000 through star.fun in approximately 10 minutes, reaching an $8 million valuation during the campaign. The capital supports engineering, secure execution infrastructure, pilot onboarding, verification research, and developer adoption for its decentralized GPU and CPU network.
Funding milestone: star.fun publicly announced that TDCC had raised $50,000 for a decentralized GPU/CPU network for AI inference and training on July 22, 2026. The 10-minute campaign duration and $8 million valuation are reported from TDCC's campaign records. View the announcement on X.
07 / Risks and principles
Trust is earned through measurable execution.
The network must address unreliable hosts, malicious behavior, model and runtime incompatibility, privacy requirements, energy economics, latency, and disputes over completed work. TDCC will prioritize workload isolation, clear acceptance criteria, auditability, reputation, failover, and transparent limits over unsupported promises.
Build with TDCC.
We are looking for AI companies, data centers, GPU operators, researchers, and infrastructure partners who want to help make distributed compute usable.
Contact the team