Free short course
Local models and inference engineering
Understand quantisation, KV caches, latency, adapters and local deployment boundaries.
This course is free in full: every module, knowledge check and the final task, with no sign-up.
Understand quantisation, KV caches, latency, adapters and local deployment boundaries.
Complete Inside language models; basic familiarity with computer memory. Each lesson includes a worked example, a practice task and an answer explanation. Allow about 12 minutes per lesson, five minutes for the knowledge check and 30 minutes for the final assignment. The reading is concise; the study time includes doing the exercises.
All scenarios are fictional. No paid subscription, specialist hardware or live AI integration is required to complete the written exercises. Advanced courses teach design and evaluation principles; they do not claim to provide a production implementation or professional accreditation.
By the end, you'll be able to…
- Produce a deployment comparison using measured constraints rather than model-size slogans.
- Map where data goes in a supposedly local setup.
- Estimate weight storage without confusing it with total memory.
- Explain why longer conversations can increase inference memory.
- Measure the performance that the task actually needs.
- Plan what must be maintained after customisation.
What's in it
- Module 1: Local models and inference engineering in practice
Produce a deployment comparison using measured constraints rather than model-size slogans.
- Finishing the course
One final task that puts the whole method to work on something real.