Lesson 1604 of 2116
AI On-Device Inference: Core ML, ONNX Runtime, MLC LLM
On-device LLM inference is now feasible on phones and laptops — the platform choice constrains model size, format, and update cadence.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2on-device inference
- 3Core ML
- 4ONNX
Concept cluster
Terms to connect while reading
Section 1
The premise
AI can compare on-device inference platforms for your target devices, but mobile and desktop integration work is engineering-owned.
What AI does well here
- Draft platform comparison matrices on supported models, quantization, and platform reach.
- Generate device-tier benchmarking plans.
What AI cannot do
- Replace mobile-platform engineering work.
- Predict thermal and battery behavior without device tests.
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “AI On-Device Inference: Core ML, ONNX Runtime, MLC LLM”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 45 min
Structured Outputs: Make the Model Return Data You Can Trust
For production apps, pretty prose is often the wrong output. Learn when to use structured outputs, function calling, and schema validation.
Creators · 9 min
Pro Search vs Default: When To Spend The Compute
Pro Search runs more queries, reads more pages, and routes to a stronger model. It is not always worth the wait — knowing when it is is the skill.
Creators · 10 min
Perplexity API: Building RAG Without Owning The Pipeline
The Perplexity API gives you cited search answers with one call. It is the cheapest way to add grounded retrieval to a product — and the limits are worth understanding.