Frontier AI interview practice

STABLE PUBLIC PREVIEW

DeepInfer

DeepSeek's production inference framework as an interview case study: asynchronous scheduling, expert and data parallelism, speculative decoding, SWA bounded replay, deterministic sampling, structured outputs, and NVIDIA or Ascend execution. Compare the released offline engine with the KV-cache system, prefill/decode disaggregation, and replica load balancing still needed for a complete serving service.

Read the free DeepInfer architecture guide

Official sources

DeepInfer repository ↗
Implementation, supported hardware, prerequisites, examples, benchmarks, and release boundaries.
DeepSeek inference deployment Q&A ↗
Production architecture, expert and data parallelism, prefill/decode disaggregation, KV-cache storage, and load balancing.

Practice materialChecking account access…

Upgrade

The public brief is identical for people and crawlers. Account access changes tools, not the indexed description.

PRODUCT FEEDBACK

Was this practice useful?

Rate this surface from 1 to 5. A rating sends the score, an allowlisted feature key, and random event/device identifiers; when signed in, the server associates it with your account and tier. It sends no code, notes, prompt text, email, URL, IP-address field, or user-agent field.

Share privacy-safe automatic feature-use and browser performance measurements from this browser. Performance is counted by route family and mobile/desktop viewport class. Local code and workspace content are never included. Ratings are sent only when you press a rating button, independent of this setting. Share signed-in page and question time, visible Python or notebook coding-pane time, and selected practice actions for product improvement. The operator can review these by account and question. This is off by default. Code, typed text, and workspace content are excluded. Raw events are kept for up to 30 days.