Save up to 95% on token costs with your own hardware
Cascadia turns the commodity Intel AI PCs sitting on your employees' desks into a private LLM inference cluster — no cloud contracts, no new hardware, no data leaving your network.
Built for teams who can't send their data to someone else's GPU
Finance, healthcare, legal, defense.
Compliance regimes that treat any third-party inference as a data egress event. Cascadia keeps the entire prompt-to-token path on your network.
Existing fleets of Intel AI PCs.
Organizations that have already made the capex decision on modern laptops. Cascadia is the software layer that makes that hardware a productive AI asset.
Teams watching cloud GPU burn.
Engineering orgs where LLM inference has become a line item nobody can forecast. Fleet inference reframes the cost as capex you’ve already absorbed.
Air-gapped and restricted networks.
Environments where cloud inference isn’t permitted at all. Cascadia runs entirely on your infrastructure — no outbound dependencies, no phone-home.
The compute you've already bought is idle most of the day
Every AI PC on your team's desk runs spreadsheets and video calls.
A 500-laptop fleet is the AI-compute footprint of a small GPU cluster, running at single-digit utilization most of the day.
Your cloud GPU bill pays for compute you already own.
Calculate your fleet's savings →Coordinated inference across your existing fleet
Cascadia compiles a shard for each node in the fleet.
Cascadia's export pipeline splits a model into pre-compiled INT4 shards — one per node. Each shard is a self-contained OpenVINO graph. No runtime partitioning, no framework surgery.
Cascadia installs on Intel AI PCs your team already uses. It pre-compiles model shards, distributes them across nodes, and coordinates inference over your existing network.
Explore the architecture →Cascadia occupies a space nobody else does.
Cascadia is the only distributed inference runtime purpose-built for Intel AI PC fleets on Windows. Every other system in this space targets a different substrate.
| System | Hardware | Distributed inference | Data path & privacy | On-prem & sovereignty | NPU |
|---|---|---|---|---|---|
 | Intel AI PC · CPU+NPU+iGPU | Cross-WAN, heterogeneous fleet | On-fleet · activation compression | Customer-owned fleet | NPU-aware routing |
| Darkbloom | Apple Silicon only | One Mac per request | Stranger's Mac · Secure Enclave | Cloud marketplace | |
| Petals | NVIDIA / AMD GPU | Volunteer swarm | Plaintext public swarm | Self-host, no enterprise story | |
| Parallax | NVIDIA + Apple Silicon | DHT pipeline scheduler | Activations cross peers | Self-host unproven | |
| Cloud APIs | Hyperscaler GPUs / TPUs | Hidden / homogeneous | Provider data center · TLS | VPC tier only | |
| Exo Labs | Apple Silicon · CUDA · ROCm | LAN dynamic partitioning · no WAN | Stays on user cluster | Customer hardware | |
| Prime Intellect | NVIDIA consumer GPUs | ~100 ms WAN-tolerant | Permissionless swarm | Permissionless cloud |
Data-center throughput on desk-class hardware
Llama 3.1 8B INT4 across two commodity Intel AI PCs, distributed over a standard office WiFi network — with 2-stream micro-batching and K=3 speculative decoding. No GPU servers, no data center.
Dig into the methodology →