AI & Data Centers
The Request Has to Arrive Before Anything Can Be Computed.
Organizations running or consuming AI compute carry a network segment on both legs of every request. ACME manages that segment. Network paths are optimized for AI and inference workloads; the model, GPU and inference engine are separate variables.
The applications that depend on the network
- Interactive inference and agent traffic between users and compute
- Distributed inference across regions and availability zones
- Data movement between sites, colocation and cloud compute
- Voice and multimodal services with strict responsiveness needs
- Operations and monitoring traffic across facilities
The network variables that put them at risk
- LATENCY
- Perceived response time grows before compute is even reached.
- TAIL LATENCY
- The slowest requests define how the service feels.
- PACKET LOSS
- Retries add delay that looks like a compute problem.
- ROUTE QUALITY
- Cross-region access varies without anyone measuring why.
WHERE ACME SITS
- Between the consuming environment and the AI or cloud destination
- Across regions where distributed inference is served
- At facility edges alongside existing transit and interconnect
WHAT IS MEASURED
- Round-trip time to each compute destination
- Loss, jitter and congestion per route
- Route selection history and stability
- The network contribution to end-to-end response time
WHAT CHANGES
- A clear separation of network conditions from compute behavior
- Steadier response characteristics for interactive AI services
- Measurement that can be correlated with workload metrics you already track
Technology protected by 11 real-time communications patents. Performance examples on this site are illustrative and are not customer performance claims.