Enterprise AI
Leadership, architecture and delivery at organisational scale.
Local AI first ≠Local AI only
Does every AI workload really need the cloud?
I experiment with real systems to find what should run Local, Hybrid or Cloud, proving trade-offs with reproducible benchmarks and code.
Principal AI Engineer & AI Strategy LeadExplore method & systems
Where should AI run?
Start with the workload, not the model. Privacy, latency, control, capability and scale tell you whether Local, Hybrid or Cloud makes sense.
| Decision factor | Local AILocal-first lens | Hybrid | Cloud |
|---|---|---|---|
| PrivacyWhere data is processed | Local AIStrength: Stays on device | HybridSplit by policy | CloudProcessed remotely |
| Network & latencyConnectivity dependency | Local AIStrength: Works without network | HybridLocal fallback | CloudNetwork required |
| Runtime controlDeployment & model lifecycle | Local AIStrength: You control the runtime | HybridShared control | CloudProvider managed |
| Trade-offsWhere Local AI trades capacity for control | |||
| Model capabilityLargest model available | Local AIBound by device | HybridStrength: Routes by workload | CloudStrength: Frontier models |
| Elastic scaleBurst capacity | Local AIFixed by hardware | HybridStrength: Cloud overflow | CloudStrength: Elastic |
| Best fit | Local AIPrivacy, latency & control | HybridVariable workloads | CloudMaximum capability |
Analyzing an AI workload?
I share architecture analyses and trade-off breakdowns regularly.
01 DECIDE 02 BUILD
Making local inference actually usable.
When Local or Hybrid makes sense, I build the runtime products need across desktop, mobile, and speech.
02 BUILD 03 TEST
Does it work in a real product?
Infrastructure only matters when a real application can depend on it.
Can sensitive document processing stay local?
WHAT THIS VALIDATES:
PII redactionHuman reviewPrivacy boundary
Can personal transactions be categorized on-device?
WHAT THIS VALIDATES:
On-device parsingDeterministic mathZero telemetry
Can meeting intelligence stay inside the room?
WHAT THIS VALIDATES:
Local audio ASRSpeaker contextOpt-in cloud
03 TEST 04 MEASURE
Performance Lab
Can this workload run locally?
Performance Lab benchmarks the real model on real devicesto turn evidence into a Local, Hybrid or Cloud decision.
Work in progress: representative benchmark dataset in active collection across target hardware.
Work in progress
Real evidence - my devices
Empirical benchmarkWhat matters
Speed
Memory
Stability
Quality
TTFT
Tokens/s
Peak RAM
Thermal
Task success
MacBook Pro M3 (36GB)Model: Nemotron Nano 4B Q4
0.24 s
34
3.1 GB
58 °C
91%
GoodFast, smooth generation and low thermals.
Samsung Galaxy A56 8GBModel: Qwen 3.5 2B Q4_K_M
0.62 s
13.5
1.9 GB
41 °C
76%
ReviewViable for targeted tasks; slower on complex context.
Deployment Synthesis
These results determine where the workload should run
LocalAll critical thresholds pass.
HybridLocal handles the main flow; harder cases need fallback.
CloudA hard requirement is not met locally yet.
Who I am
AI strategy. Hands-on engineering. Local AI.
I lead enterprise AI systems and build AI on real devices to understand what should run Local, Hybrid or Cloud.
Hands-on engineering
AI systems built and tested on Mac, Android and local infrastructure.
Public work
Open-source systems and technical writing through GitHub and stAI tuned.