Inference engineers make large language models fast and cheap to serve in production: writing and tuning GPU kernels, managing KV-cache memory, batching requests, quantizing weights, and operating engines like vLLM, SGLang, and TensorRT-LLM against latency and cost targets. It's one of the fastest-growing specialist roles in AI infrastructure.

The core inference engineering skills are transformer internals (attention, KV caching, sampling), GPU architecture and CUDA or Triton kernel writing, quantization, continuous batching and paged attention, distributed serving (tensor and pipeline parallelism), and profiling with tools like Nsight. InferQuest's roadmap covers all of these in order, with a verifier gating each major skill.

Yes — the Model Training path covers exactly that: backprop and optimizers from scratch, data curation with real Common Crawl pipelines, scaling-laws math, the NanoGPT-speedrun efficiency toolkit (Muon, FP8, fused kernels), a GPT-2-class pretraining capstone you can run on one consumer GPU or ~$50 of rented compute, then SFT, LoRA, DPO, and GRPO post-training on a single GPU. It leads to the pretraining, post-training, and RL engineering roles labs are actively hiring for.

InferQuest is completely free and open. There is no paper certificate — instead, milestones are auto-verified: live probes against your deployed endpoint, GPU-graded kernel submissions, and merged-PR checks against real open-source repos. The result is a portfolio of receipts, which hiring teams weigh far more than a certificate.

Both paths together span 182 tasks across 38 quests (21,740 XP), sharing a common trunk of fundamentals. An experienced software engineer studying part-time should expect roughly six months to a year for one path end to end — less if you already know PyTorch and CUDA, since early phases are skimmable.

For the kernel-engineering phases, yes — the grading harness runs on your own hardware, and any modern NVIDIA GPU works. Everything before that (transformer internals, the inference-engine capstone, quizzes and drills) runs on CPU or free cloud notebooks.