Cascadia AI Systems Lab
Independent AI Infrastructure Research & Development Environment
Mission
Build practical, self-hosted AI infrastructure that works.
Cascadia AI Systems Lab is a long-term infrastructure laboratory focused on designing, building, and operating practical AI systems at the intersection of GPU computing, data engineering, and modern infrastructure.
Core Focus Areas
Infrastructure Design
AI infrastructure patterns for GPU workloads, model serving, and data processing
Storage Architecture
Optimized storage systems for AI/ML data patterns and distributed filesystems
Performance
Real-world performance characteristics of hardware and architectural decisions
Experimentation
Hands-on research with GPU optimization, model serving, and cluster computing
What We Build
Experiments
- • GPU acceleration and CUDA optimization
- • Model serving architectures (vLLM, Triton)
- • Data pipeline design and orchestration
- • Storage systems and filesystem benchmarking
- • Virtualization and containerization strategies
Infrastructure
- • System provisioning and configuration management
- • Monitoring, observability, and alerting
- • Cluster orchestration and resource scheduling
- • Network topology and performance tuning
Tooling
- • Diagnostic and debugging tools
- • Performance benchmarking suites
- • Deployment and lifecycle management scripts
- • Custom utilities for lab operations
How This Differs from a Software Project
Infrastructure-First
The primary artifact is not code, but running systems and operational knowledge
Experiment-Driven
Many experiments may fail or be abandoned—that's expected and valuable
Hardware-Aware
Decisions are made in the context of specific physical infrastructure
Documentation-Heavy
Writing down what was learned is as important as building the system
Current Status
This lab is in active development. Expect frequent changes to structure, tooling, and documentation as the infrastructure matures.