Centralized compute
Hyperscale data centers increasingly control access to the GPUs that power modern intelligence.
Koinos AI gives people and developers a path out of centralized AI dependency: run AI on hardware you own, keep sensitive work private, and scale into community compute when you need more.
Today's dominant AI model puts the APIs, hardware, pricing power, and enormous amounts of user data under the control of a few corporations.
Hyperscale data centers increasingly control access to the GPUs that power modern intelligence.
Private conversations, files, source code, and business context routinely leave hardware you control.
Developers build core products around external APIs whose prices, limits, and rules can change at any time.
When access to intelligence is permissioned by a few companies, everyone else builds on rented ground.
Use your own machine first. Add trusted private hardware when you have it. When you need more capacity, tap into a distributed network of compatible providers rather than defaulting to a hyperscaler.
Keep the familiar API surface while moving control of compute, routing, privacy, and economics back into infrastructure you own.
Run an OpenAI-compatible Koinos AI gateway on your workstation, server, or private infrastructure.
client = OpenAI( base_url="http://localhost:8181/v1", api_key="kai_local_••••" ) response = client.chat.completions.create( model="koinos-smart", extra_body={"routing": "local-first"} )
Your GPUs serve requests first. If your capacity is exhausted, your routing policy can selectively use private pools or the decentralized public network.
Apps do not need different architectures for local inference and elastic network inference. Koinos AI handles the routing layer underneath.
Instead of requiring every GPU to live in a giant data center, Koinos AI lets compatible consumer and professional computers contribute useful AI work. The software handles benchmarking, model setup, recommended pricing, and resource protection automatically.
Schedulers match workloads with eligible hardware using model availability, performance, reputation, privacy requirements, latency, and economics. Koinos handles ownership and settlement beneath the experience.
Gaming PCs and workstations can contribute capacity when their owners are not using it.
Organizations combine hardware they trust and selectively decide whether public overflow is permitted.
Server and GPU fleet operators connect through the same protocol with advanced controls.
Inference comes first, followed by evaluation, fine-tuning, distillation, and increasingly capable distributed training.
Koinos AI Core can do more than provide AI compute. An optional Koinos node module lets the same machine support the blockchain underneath the AI economy—and advanced users can opt into Proof-of-Burn block production as a separate KOIN earning opportunity.
Let Koinos AI automatically benchmark compatible hardware, route useful AI jobs, protect foreground use, and settle verified work through KAI.
Install the optional full-node module and, if you choose, configure Koinos Proof-of-Burn block production. Block production remains governed by Koinos and requires KOIN/VHP.
Clean incentive design: KAI rewards useful AI and Koinos-AI infrastructure services. KOIN rewards Koinos consensus. We do not double-pay KAI simply for producing blocks.
KAI is designed to coordinate the network economy—not meter AI running on your own hardware.
Pay for third-party inference, verification, future training, specialized models, and other network services.
Providers receive KAI for successfully completed work rather than simply leaving a process online.
Verified model creators may eventually receive bounded usage royalties when their models serve paid network workloads.
Token supply, fee splits, emissions, bootstrap pricing, and liquidity parameters remain provisional pending economic simulation and legal review.
Decentralized compute is not automatically private. Koinos AI makes execution policy explicit instead of hiding that distinction.
Your machine. Your model. Data stays on the device.
Use compute owned or explicitly trusted by your organization.
Encrypted transport to reputation-bearing public providers.
Future attested confidential-compute hardware for higher-assurance workloads.
The first objective is a working local + network compute loop. More ambitious decentralization and training layers come after the core network proves itself.
No. Local AI is designed to run on your own hardware without requiring KAI. Network services can be abstracted behind familiar payment and credit experiences.
No. Entry-level providers should be able to begin with compatible hardware and sponsored Koinos resource usage. Higher-value provider classes may later use KAI collateral.
Not automatically. Standard remote workers process the workload. Local, private-pool, and future confidential-compute modes provide stronger privacy choices.
It lets developers keep their own GPUs as the primary inference layer while retaining an elastic path to decentralized capacity instead of building the product permanently around a hyperscale AI API.
Koinos AI is being built around a simple idea: intelligence should not have to live inside a handful of corporate data centers.