Intermediate~3h
Transformer Memory Math in AI Inference & ServingInterview Readiness Check
KV cache sizing formulas, activation memory, parameter memory in fp16/int8/int4, batch size vs sequence length tradeoffs, context length cost curves, and hands-on memory budget worksheets for common models..
KV cache sizememory budgetfp16 memorycontext length costparameter memorybatch memory
Learning path
Inference Fundamentals→Transformer Memory Math ← you are here→Batching Strategies for LLMsQuantization: GPTQ, AWQ, GGUF Deep Dive
Try a sample question
A Llama-3 8B model with 32 layers, 32 attention heads, and d_model=4096 serves a request with a 4096-token context in FP16. What is the total KV cache size for this single request?
A.512 MB — this would result from using GQA with 8 KV heads at FP16 precision for this context length
B.1 GB — this corresponds to a 2048-token context or a model with half the layers using full MHA
C.2 GB — computed as 2 (K+V) × 32 layers × 4096 tokens × 4096 d_model × 2 bytes = 2 GB
D.4 GB — this would correspond to FP32 KV storage rather than the FP16 precision used here
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of transformer memory math fundamentals
- Trade-offs and design decisions in real system scenarios
- KV cache size and memory budget — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the Transformer Memory Math check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the Transformer Memory Math check test?
- The check covers kv cache sizing formulas, activation memory, parameter memory in fp16/int8/int4, batch size vs sequence length tradeoffs, context length cost curves, and hands-on memory budget worksheets for common models.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for Transformer Memory Math?
- Key concepts include: KV cache size, memory budget, fp16 memory, context length cost, parameter memory, batch memory. These frequently appear in ai inference & serving interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study Transformer Memory Math?
- Most engineers spend around 3 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before Transformer Memory Math?
- Transformer Memory Math builds on Inference Fundamentals. Complete those first for the best results.
- Is KV cache size, memory budget, fp16 memory covered?
- Yes. The Transformer Memory Math module covers KV cache size, memory budget, fp16 memory among other key concepts. The check includes questions that reflect real ai inference & serving interview patterns.