Model and data
Choose the model, prepare datasets, and define the context and response your application needs.
THE TENSORBAY TOKEN FACTORY
The infrastructure behind AI tokens.
From model training to production inference, TensorBay AIFactory brings the GPU systems, networking, storage, power, cooling, and operations teams plan around AI token generation and use.
FROM MODEL TO PRODUCTION
Tokens begin with infrastructure.
AI tokens are produced when a model processes input and returns output. Each workload depends on a connected system, not one component alone.
01 02 GPU compute
GPU memory, architecture, and cluster scale shape the capacity available for training and inference.
03 Network and storage
Move model weights, prompts, and data between the systems that need them.
04 Generation and use
Applications send input and receive model output. Context length, response size, and demand change the workload.
GPU SERVERS AT THE CENTER
GPU systems sit at the heart of the factory.
GPUs run training and inference workloads. Power and cooling sustain the rack, while networking and storage keep models and data within reach. Your team operates its models and application in its chosen software stack.
GPU COMPUTETOKENOMICS
The economics of generating and using AI tokens.
Token output and workload cost depend on model behavior, infrastructure, utilization, and application demand. Plan these factors together for a clearer view of production economics.
There is no single cost-per-token figure for every model, prompt, or deployment. Measure the whole workload, then match capacity and reservation terms to how it will be used.
- 01
Model and context
Model architecture, input size, context length, and output shape influence compute requirements.
- 02
GPU architecture
GPU memory and compute capacity determine which models and workload sizes a system can support.
- 03
Utilization over time
How consistently the system is used affects planning across reserved capacity and deployment windows.
- 04
Application demand
Traffic patterns, response targets, and usage peaks help define the capacity your team needs.
PLAN THE NEXT STEP
Infrastructure for the work you need to run.
What does tokenomics mean here?
Tokenomics describes the economics of generating and using AI tokens. It brings together model behavior, compute capacity, utilization, and application demand, rather than treating tokens as a standalone infrastructure metric.
Does TensorBay provide a hosted inference API?
TensorBay provides GPU infrastructure for AI workloads. Your team runs its models and applications in the software stack it chooses.
What affects token generation in production?
Model choice, context length, GPU architecture and memory, utilization, network and storage paths, and application demand all affect the workload.
Which workloads can use TensorBay GPU infrastructure?
Dedicated GPU systems can be planned for model training, fine-tuning, research, and production inference. The right configuration depends on the workload and delivery plan.
08Give your idea room to grow
Plan the infrastructure behind your AI workload.
Tell us about your models, token workloads, capacity needs, and timeline. We can help map a practical next step.
- A configuration built around your project
- A proposal with a delivery timeline
- Acceptance testing before you start