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.

Version 1.0July 2026Seven sections

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.

For

AI companies

Get compute for training, fine-tuning, inference, and evaluation.

For

Data centers

Turn available GPU capacity into useful, recurring work.

For

GPU operators

Earn by running approved AI jobs on compatible hardware.

For

Developers

Use one API instead of building your own compute network.

03 How it works

One API. The right hardware underneath.

  1. 01

    Connect capacity

    Data centers and GPU operators share hardware, availability, and pricing.

  2. 02

    Submit a job

    An AI team sends a training, fine-tuning, inference, or evaluation job.

  3. 03

    Run it

    TDCC matches the job with hardware that fits.

  4. 04

    Verify it

    We check the result and pay hosts for accepted work.

Simple promiseAI teams bring the models. TDCC brings the compute.

04 Product

Everything AI teams need to run models.

Workload

Training

Train models on suitable distributed capacity.

Workload

Fine-tuning

Customize open models with LoRA and QLoRA.

Workload

Inference

Run batch jobs, APIs, image generation, and embeddings.

Workload

Evaluation

Test models, generate data, and compare results.

05 Roadmap

A practical path from coordination to network scale.

  1. Phase I

    Foundation

    Define workload schemas, host requirements, secure runtimes, scheduling primitives, and verification policies.

  2. Phase II

    Pilot network

    Connect selected AI companies and data centers. Validate training, fine-tuning, inference, and evaluation workflows with measurable service levels.

  3. Phase III

    Capacity expansion

    Onboard additional data centers and qualified GPU operators, improve routing, introduce reputation signals, and expand geographic resilience.

  4. Phase IV

    Production platform

    Offer 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.

$50,000

Raised through star.fun

~10 min

Approximate campaign duration

$8M

Valuation reached during the campaign

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 milestonestar.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
  • disputes over completed work

TDCC will prioritize

  • workload isolation
  • clear acceptance criteria
  • auditability
  • reputation
  • failover
  • transparent limits over unsupported promises

Partnerships

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 Back to home