WHO IT'S FOR
Developers and technical learners building, testing, and reviewing practical AI or automation systems.
MEMBERSHIPMembership / PKR 499 per monthdeveloper
AI Infrastructure & Local LLMs
Evaluate hardware, benchmark local models, optimize memory safely, and deploy a protected inference service with measured limits.
24
lessons
8
modules
16 hours
incl. practice
~4 wks
typical pace
Included with your monthly membership
Sign in with an active membership to read the lessons. Your account keeps your payment record and saved progress.
Before you start
Ye course tumse kya expect karta hai
PREREQUISITES
Comfort reading basic code and using a terminal. Check the listed tools; no specific paid subscription is assumed.
STUDY PLAN
About 4 weeks at three short sessions per week: learn, apply, then review. Practice and capstone time are included.
PAKISTAN CONTEXT
Projects favour low-cost tooling, careful data handling, and workflows resilient to local power and connectivity constraints.
TOOLSOllamaCUDALocal LLMs
Curriculum
8 modules · 24 lessons
Membership required for lesson access
1.1Local LLMs vs. Cloud APIs: A Practical Decision Framework15 minpremium
1.2Installing Ollama and Running Your First Local Model20 minpremium
1.3Understanding Model Sizes and Quantization in Plain English20 minpremium
Outcomes
Kya kar paoge
- Choose local, cloud, or hybrid inference from task quality, privacy, reliability, and full cost evidence
- Install a pinned open-weight model locally and document provenance, terms, hardware use, and limits
- Benchmark latency, throughput, quality, memory, and thermals across controlled model configurations
- Diagnose CUDA, context, concurrency, offload, multi-GPU, cache, and out-of-memory trade-offs safely
- Deploy a protected local inference API with identity, limits, observability, recovery, and handoff
Assessment: Complete the lesson exercises and capstone, then review the work for accuracy, safety, and fit. Completion is not an income or job guarantee.
Capstone
A pinned local inference service with a protected API, workload evaluation, load/recovery evidence, dated cost model, and second-operator runbook.
- Names the user, problem, scope, and acceptance test
- Identifies sources, permissions, and unknowns
- Includes the actual deliverable, not only screenshots
- Tests one normal and one failure case
- Records risks, limits, cost, and human review
- Explains what changed, what worked, and what comes next
HONEST STATUS
This course is included in platform membership. Access lasts until the expiry shown in your account; progress is retained when access expires.
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