The learning journey.

01Orient

02Build · three parallel tracks

• gpu2

  Hardware track

  “How does compute work?”

  GPU Kernel Engineering — Kernel Atlas

  Learn the machine the models run on: CUDA, Triton, GPU memory, profiling, and kernel optimization across a bilingual 12-week atlas.

  CPU vs GPU → GPU architecture → VRAM → memory bandwidth → CUDA / Tensor cores → FP32 / FP16 / BF16 / FP8 / INT8 / INT4 → matrix multiplication → CUDA kernels → FlashAttention → KV cache → quantization → multi-GPU → tensor parallelism

  gpu.aserdargun.com ↗ opens in a new tab

  Explore the companion learning tool · POL ↗ opens in a new tab
  How do languages express the same program differently?
  English-only comparisons of the same tasks across programming languages: types, memory, paradigms and concurrency. Programming foundations that support GPU learning, but are not limited to GPU. Executed examples and hand-reviewed examples remain distinct.

  Try the companion lab · GEX ↗ opens in a new tab
  How does a kernel become hardware execution?

• llm3

  Serving track · hub

  “How do models run?”

  LLM Runtime & Serving Atlas

  Learn to ship models: runtime and serving solutions classified across seven architectural layers. Hardware and training both feed this track.

  model → architecture → precision → memory calculator → runtime → inference engine → serving → API → benchmark

  Ollama · llama.cpp · vLLM · SGLang · TensorRT-LLM · Transformers · MLX

  llm.aserdargun.com ↗ opens in a new tab

  Try the companion lab · TFL ↗ opens in a new tab
  How do finite serving resources shape a request’s path to its next token?

• usl4

  Training track

  “How do models learn/change?”

  Unsloth Studio Learning Atlas

  Learn to change models, not just run them: Unsloth, LoRA, QLoRA, dataset engineering, and evaluation in a bilingual learning atlas.

  pretrained model → dataset → tokenization → LoRA → QLoRA → SFT → DPO → GRPO → evaluation → merged model → LLM runtime

  usl.aserdargun.com ↗ opens in a new tab

  Try the companion lab · ADP ↗ opens in a new tab
  How does a base model become adapted to a task?

gpu → llm hardware decides what you can serve · usl → llm the models you train become what you serve · llm → gpu inference pressure exposes the hardware limits

03Engineer · agent system

04Engineer · context and assurance

• ctx6

  Context & Knowledge Engineering

  “What does the model actually see?”

  Context & Knowledge Engineering

  Design the information system that runs before model generation. Trace ingestion, chunking, embeddings, retrieval, citations, caching, memory, and tool use so the model sees grounded, governable context.

  ingestion → chunking → embeddings → vector DB → retrieval → citations → cache → memory → tool use → token budget

  ctx.aserdargun.com ↗ opens in a new tab
• sec7

  AI Systems Security

  “Why should I trust this agent system?”

  AI Systems Security Observatory

  Trace delegated intent from model capability to auditable action. Examine identity, credentials, authorization, tool and sandbox boundaries, protected data, audit evidence, and incident recovery without collapsing assurance into a score.

  model → agent → identity → credential → authorization → tool → sandbox → data → action → audit → incident

  sec.aserdargun.com ↗ opens in a new tab
• evl8

  AI Evaluation & Reliability

  “How do I know it works?”

  AI Evaluation & Reliability Lab

  Close the loop with evidence. Define evaluation contracts across output, trace, outcome, robustness, safety, and operations so every release becomes a deterministic, measured decision rather than an assumption.

  golden set → output → trace → outcome → robustness → safety → operations → release decision

  evl.aserdargun.com ↗ opens in a new tab

ctx → sec context decides what is allowed in, security decides what is allowed out · sec → evl assurance surfaces the questions evaluation must answer · evl → ctx measurement feeds the next round of context design

05Deploy · local and cloud in parallel

• lcl

  Local AI Lab

  “Which local lab should I buy?”

  Local Compute Lab

  Match open-weight model requirements to NVIDIA, AMD, and Apple devices. Compare workload, memory, privacy, power, noise, and existing equipment before choosing a local lab.

  open-weight model → workload → memory → NVIDIA / AMD / Apple → privacy / power / noise → local lab

  lcl.aserdargun.com ↗ opens in a new tab
• cld

  AI Infrastructure

  “How do I operate this at scale?”

  Cloud Provider Cost Comparison

  Turn the stack into decisions: compare pre-tax USD costs of cloud services available from Türkiye and choose where the whole system runs. Cloud comes last on purpose — once you know what you deploy, infrastructure stops being abstract.

  model → vLLM → Docker → GPU instance → cloud GPU → load balancer → autoscaling → API

  cld.aserdargun.com ↗ opens in a new tab

06Return

The loop closes when what you learned returns to the system — the map you started with becomes the record of what you can now build, and the next pass through the loop starts deeper.