Distributed compute for real-time inference
FAR AI is a distributed inference layer built for interactive workloads. Instead of relying on centralized clusters, it activates existing GPUs and routes requests to the best available nodes in real time.
Built for real usage
Built for teams that need AI to respond in real time, not in demos.
Game studios
Keep matches alive with human-like AI even when players drop out. FAR AI delivers low-latency responses suitable for live gameplay.
AI products
Power high-frequency AI features without relying on a single provider. FAR AI stays responsive under load and scales with demand.
Node operators
Connect your gaming GPU to a real production network and support real-time inference workloads running across the system.
Why centralized inference is limited for real-time AI?
Capacity expands slowly
New clusters take time to build, then demand spikes faster than supply follows
Access concentrates
When supply is narrow, pricing and availability sit in the hands of a few owners
Idle GPUs stay unused
Gaming-grade GPUs already exist in millions of PCs and spend most time underused
How FAR AI works differently
Distributed compute
Instead of building datacenters first, the network uses hardware that already exists and is ready today at scale.
Routing built for latency
Requests route to nodes that fit the workload and sit closer to demand, so inference stays responsive under load.
Verification built in
The network runs checks that validate real computation then uses the results to protect routing quality at scale.
How a node works
Estimate node output