deepseek
AI infra interview questions tagged deepseek, across every topic.
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Concepts behind "deepseek"
The curriculum that explains the ideas these questions test.
Foundational
Open-Weights Models of 2026The open-weights frontier moved from dense models of tens of billions of parameters to sparse mixtures of experts measured in trillions, and the serving problem changed with it. As of September 2026 the releases an infrastructure engineer is asked about are Z.ai's GLM-5.3 at 753B, Moonshot's Kimi K3 at 2.8T, and DeepSeek's V4 family. What matters for deployment is not the headline count but three other numbers: active parameters per token, the attention design, and the format the weights actually shipped in.🧮 Open Weights & Serving Engines
Core
Attention Variants: MHA, GQA, MQA and MLAThe KV cache scales with the number of key-value heads, and the four attention variants differ exactly there: multi-head keeps one KV head per query head, multi-query keeps one for all, grouped-query shares one across a group, and multi-head latent attention caches a compressed latent instead of keys and values at all. For Llama 3.1 70B that is the difference between 2.6 MB and 320 KB per token; for DeepSeek-V3 it is about 70 KB. The variant a model was trained with is a serving decision made before the first GPU was bought.🚀 Inference & ServingSign in
Advanced
Expert Parallelism for MoEA mixture-of-experts layer runs only a few of its experts per token, so the experts can be spread across GPUs and each token shipped to the ranks that hold its chosen experts. That shipping is an all-to-all in each direction, twice per layer per pass, and its cost plus the load imbalance between experts is what expert parallelism is really about.🕸️ Distributed Training🔒 Premium