One trajectory.
Power to intelligence.
Comet Digital AI runs a single trajectory from leased megawatts to billed tokens — powered data-center capacity and NVIDIA GPU clusters on one end, a hardware-invisible inference API on the other, built for teams shipping copilots, support agents, and document pipelines.
Infrastructure: the launch stage.
Comet Digital AI takes over data-center shells that already have power agreements in place, fits them with NVIDIA GPU clusters, and rents capacity out on multi-year take-or-pay terms — skipping the years-long queue for a new grid connection.
Lease the shell
Take over existing data-center shells that already come with power agreements negotiated — the single constraint that usually adds years to a build-out.
Install the clusters
Fit those shells with NVIDIA GPU clusters, racked and networked for production AI workloads from day one.
Contract the capacity
Rent capacity out through multi-year take-or-pay contracts, sized to actual demand rather than sold as raw colocation.
Inference: where the light reaches you.
On top of that infrastructure, Comet Digital AI runs a fleet of fine-tuned open models behind one API, billed per million tokens. Customers write code against a familiar client and never have to think about the hardware underneath.
Fine-tuned open models, tuned for tokens-per-watt
A fleet of fine-tuned open models — Llama and Mistral-class — run on Comet Digital AI's own inference stack, optimized for tokens delivered per watt rather than raw throughput.
One API, five familiar client formats
Customers connect using OpenAI-, Kimi-, Claude-, Grok-, or Gemini-compatible clients and pay per million tokens. The hardware underneath never has to be named.
Built for workloads that never stop
Coding copilots, customer-support agents, and document/RAG pipelines: high-volume, latency-sensitive, cost-per-call-sensitive — the traffic pattern Comet Digital AI is tuned for.
A comet doesn't burn brighter. It burns efficiently.
Once a shell is leased, its power is contracted, not metered by the spot market — the cost of electricity is close to fixed. That flips the usual question: instead of asking how many tokens a cluster can produce at full tilt, Comet Digital AI asks how many tokens it can produce per watt of that fixed draw, since that number is what actually moves the margin.
Optimizing for raw throughput pushes clusters to peak output regardless of power cost. Optimizing for tokens-per-watt fine-tunes model and serving choices against a fixed power budget instead, so efficiency gains show up directly as margin rather than as a cost passed through to the customer.
If your code already talks to a model, it already talks to Comet Digital AI.
Point an OpenAI-compatible client at Comet Digital AI and swap models by changing a string, not your integration.
from openai import OpenAI client = OpenAI( base_url="https://api.cometdigital.ai/v1", api_key=COMET_API_KEY ) completion = client.chat.completions.create( model="comet-llama-70b-ft", messages=[{ "role": "user", "content": "Summarize this support ticket." }] ) print(completion.choices[0].message.content) # billed per million tokens — no cluster to manage
Leasing capacity, or building on the API?
Tell us which orbit you're here for — Infrastructure capacity or Inference access — and we'll route you to the right team.