// HACKER NEWS — CYBERSECURITY
Show HN: Free Inference Engineer and Model Training Roadmap
InferQuest — verified paths into LLM serving and training
Two free, open roadmaps built from real job-market research: make models fast and cheap in production, or make them as good as possible on the cheapest hardware — with milestones that are verified, not checked off.
The full curriculum is open to browse — sign in (free) to track progress, take the drills, and unlock the verifiers.
InferQuest is a free, open, non-commercial web application for learning inference engineering and LLM training. It offers two paths — serving large language models fast and cheaply, and training them as good as possible on minimal hardware — organized into quests and tasks. It tracks your progress with XP, levels, and streaks, drills you with graded quizzes and spaced-repetition reviews, and automatically verifies major milestones like deployed endpoints, GPU kernels, training runs, and merged open-source pull requests.
Signing in (with Google or email) is used only to save that progress to your account — see the privacy policy.
Deploy an OpenAI-compatible endpoint — your own engine, then production vLLM — and InferQuest probes it for real: streaming framing, usage accounting, max_tokens cutoffs, error shapes, latency targets.
A local harness grades your kernels — attention, KV cache, Triton softmax, flash attention, quantizer, ring all-reduce — AND your training runs: first convergence, a measured ≥1.5× speedup, an adapter fine-tune that must not forget, all under fixed token budgets on your own hardware.
The open-source milestones verify against the GitHub API that your PRs into vLLM, SGLang, FlashInfer, TRL, torchtitan, nanochat & co. actually exist, actually merged, and aren't typo fixes.
KV-cache sizing math, rooflines, speculative-decoding acceptance, scaling-laws and data-curation calls, parallelism tradeoffs — graded server-side, answers never shipped to your browser.
Everyone starts in Foundations — transformer internals, GPU architecture, kernels — then branches. Level up from Token to Foundation Model on one shared XP ladder; the path titles — Inference Engineer, Training Engineer — are earned as certificates.