APPLIED AI RESEARCH
Does every AI workload really need the cloud?
I build real systems to find out. I test Local, Hybrid, and Cloud options to measure what actually works.
01 DECIDE
I don't start from the model, I start from the workload. What are the privacy requirements and latency constraints? That tells you where the model should run.
| DECISION CRITERIA | LOCALFocus | HYBRID | CLOUD |
|---|---|---|---|
Data PrivacyData egress & telemetry | Full Boundary | Partitioned | External VPC |
Offline & LatencyNetwork dependence & RTT | Zero Network (0ms) | Edge Fallback | Network Bound |
Runtime ControlModel weights & lifecycle | Full Ownership | Shared Policy | Provider API |
Frontier ReasoningAccess to 400B+ models | Hardware Bound | Dynamic Route | Frontier Scale |
Elastic ScaleBurst capacity on demand | Fixed Hardware | Tiered Offload | Unlimited Burst |
| BEST FOR | Full privacy & on-device control | Tiered triage & split routing | Frontier reasoning & elastic scale |
LOCAL / HYBRID / CLOUD decided →
02 BUILD
When Local or Hybrid makes sense, I build the runtime products need across desktop, mobile, and speech.
Runtime layer running →
03 TEST
Infrastructure means nothing until it runs in a real product. My apps test privacy, usability, and integration in practice.
Can sensitive document processing stay local?
Can personal transactions be categorized on-device?
Can meeting intelligence stay inside the room?
Products working →
04 MEASURE
Running locally isn't the goal, performing well is. I measure real numbers to shape the next architecture decision.
Which configuration is good enough for this workload on this device?
Did local AI processes stay on-device without reaching the internet?
ABOUT & TRACK RECORD
I build local AI infrastructure and test it inside real products. The goal: understand where running locally actually creates value.
Bounded memory, explicit failure modes, and reproducible environments.
Apple Silicon MLX, Android NPU/NDK, open-weight GGUF architectures.
Thinking about moving an AI workload off the cloud? Here is what I do:
I evaluate your privacy needs, latency limits, and compute costs to give you a clear Local, Hybrid, or Cloud recommendation before you spend weeks building.
I test target models directly on your real hardware (Apple Silicon, Android, servers) measuring generation speed, RAM limits, and thermals.
I design the inference runtime: model loading, memory management, quantization (GGUF/MLX), and automatic fallback to cloud.
I capture network traffic at the process level to mathematically verify zero outbound data and give you audit-ready privacy proof.
