Intermediate~3h
GPU Architecture for Inference Engineers in AI Inference & ServingInterview Readiness Check
A100 vs H100 vs H200 vs B100 specs, NVLink vs PCIe bandwidth, memory hierarchy (HBM3, L2, SM registers), CUDA cores vs Tensor Cores, SXM vs PCIe form factors, and sizing hardware for LLM serving workloads..
H100A100B100NVLinkHBM3Tensor CoresGPU sizingSXM
Learning path
Try a sample question
What is the primary memory type used in NVIDIA A100 and H100 GPUs that enables their high memory bandwidth?
A.GDDR6X — used in consumer GPUs like RTX 4090, offering around 1 TB/s bandwidth on a 384-bit bus
B.DDR5 ECC — used in server CPUs and high-reliability workstations, not GPU on-package memory
C.LPDDR5 — used in mobile SoCs and laptops for low-power operation, not datacenter GPU accelerators
D.HBM2e / HBM3 — stacked high-bandwidth memory delivering up to 2 TB/s (A100) and 3.35 TB/s (H100)
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What this module tests
- Conceptual understanding of gpu architecture for inference engineers fundamentals
- Trade-offs and design decisions in real system scenarios
- H100 and A100 — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the GPU Architecture for Inference Engineers 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 GPU Architecture for Inference Engineers check test?
- The check covers a100 vs h100 vs h200 vs b100 specs, nvlink vs pcie bandwidth, memory hierarchy (hbm3, l2, sm registers), cuda cores vs tensor cores, sxm vs pcie form factors, and sizing hardware for llm serving workloads.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for GPU Architecture for Inference Engineers?
- Key concepts include: H100, A100, B100, NVLink, HBM3, Tensor Cores, GPU sizing, SXM. 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 GPU Architecture for Inference Engineers?
- 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 GPU Architecture for Inference Engineers?
- GPU Architecture for Inference Engineers builds on Inference Fundamentals. Complete those first for the best results.
- Is H100, A100, B100 covered?
- Yes. The GPU Architecture for Inference Engineers module covers H100, A100, B100 among other key concepts. The check includes questions that reflect real ai inference & serving interview patterns.