Train, fine-tune, and run AI models on a verifiable network built for AI.
TDCC connects AI companies to data centers and available GPUs. Submit a job through one API. We find the right hardware, run the job, and verify the result across an open, ecosystem-neutral compute layer.
The shift: from renting machines to receiving verified AI work.
The strongest AI products hide an awkward layer of complexity. TDCC is being built around a simple change in the unit of value: instead of starting with a machine, start with the work you need completed.
PRODUCT REVEAL·WORKLOAD-FIRST·EARLY ACCESS
The product unit is the accepted AI job.
AI teams should be able to describe a workload, not assemble infrastructure around it. TDCC routes the job to suitable capacity, runs it in an approved environment, and evaluates the result against clear acceptance criteria.
The interface asks for the job. The network carries the machine-level coordination.
Where TDCC fits
01 / Research layer
Exa makes web knowledge easier to ask for.
Exa is a useful reference point: describe a question, then receive organized information from the web. It changes how a research request begins.
02 / Capacity layer
Vast makes GPU capacity easier to access.
Vast exposes a broad GPU marketplace where a user can find and rent an instance. It changes how a compute rental begins.
03 / Work layer
TDCC is building for the accepted outcome.
A job is matched to suitable capacity and evaluated against its acceptance criteria. In TDCC's intended model, settlement follows only after useful work is accepted.
Different layers, not a head-to-head benchmark.Exa focuses on web intelligence. Vast exposes GPU capacity. TDCC is being built as an execution and verification layer between workload intent and accepted AI work.Engineering note: two laptops, one 27B workload, 80 tok/s
Mission
AI needs more compute. We make it easier to access.
AI teams need more GPUs to train and run models. Data centers have capacity, but finding the right hardware and managing it is hard.
TDCC connects both sides in one simple network. Companies get compute. Data centers get more demand. GPU owners get paid for useful work.
One simple API
Send your AI job once. TDCC handles the infrastructure.
Use more GPUs
Connect data centers and independent GPU operators to real demand.
Verify the result
Every completed job is checked before it is accepted.
Built for AI
Train, fine-tune, evaluate, and run models in one place.
The network
One API. The right hardware underneath.
GPUs are different. TDCC matches every job with hardware that fits its model, memory, speed, price, and reliability needs.
01
Submit
Send a training, fine-tuning, or inference job through the TDCC API.
02
Match
TDCC finds a data center or GPU that fits the job.
03
Run
The job runs in an approved environment.
04
Verify
We check the result. Hosts are paid for accepted work.
AI teams bring the models. TDCC brings the compute.
Ecosystem fit
One compute layer. Different strengths, clearly separated.
TDCC is chain-agnostic where it should be: workloads run in approved compute environments, not on a blockchain. The surrounding coordination and application layers can use the ecosystem that best fits the job.
Avalanche
A natural home for verifiable coordination, policy-aware execution, and customizable infrastructure. TDCC can use Avalanche L1s for auditable workload state, provider coordination, and deployments that need explicit control over rules and participants.
Solana
A natural home for high-throughput user experiences around AI. TDCC can support Solana builders with fast agent actions, frequent job events, low-friction payments, and developer-facing applications that need responsive execution at scale.
Shared principle: Avalanche and Solana are complementary integration paths around the same verified compute service. TDCC does not force every workload into one chain; it makes useful AI compute accessible to both communities through a consistent API.
Initial workloads
Everything AI teams need to run models.
Start small with one job or scale across multiple data centers. TDCC is designed for practical AI workloads.
TDCC is a network that connects AI teams with data centers and GPUs for training, fine-tuning, and inference.
Do I need to manage a GPU?
No. Send your job through the API. TDCC handles routing and execution.
Can data centers join?
Yes. Data centers and GPU operators can provide capacity and earn from accepted AI jobs.
What makes TDCC different?
We sell verified AI work, not raw GPU hours.
How does TDCC fit Avalanche and Solana?
TDCC stays chain-agnostic at the compute layer. Avalanche fits verifiable coordination and customizable infrastructure, while Solana fits high-throughput developer, agent, and micro-workflow experiences. They are complementary integration paths, not competing compute backends.
Early access
Be early to the compute network.
Enter your early believer code to check access to the first TDCC cohort.
We are also looking for AI companies, data centers, GPU operators, ecosystem partners, and new teammates.
