116. Design an LLM Pretraining Pipeline
Pretraining a base LLM on a fixed budget: sizing by 6ND and scaling laws, the data factory, tokenizer, stable runs, failures and evals.
ClassicMedium
Designs
Pick a system. Work through the problem. Compare your approach.
Designs with worked examples 7
Ordered by design numberCompany tags are community-reported. Counts on cards show how many people reported that design.
Generative AIClear filters
119. Design an RLHF and Preference-Tuning Pipeline
Preference tuning as a weekly loop: rater and AI labels, Bradley–Terry reward models, DPO and PPO with a KL leash, vLLM rollouts, reward-hacking checks.
ClassicMedium120. Design a Fine-Tuning Service (SFT and LoRA)
Tenants upload examples and get a tuned, gated model on the same API: LoRA and QLoRA arithmetic, chat templates, Kueue fair sharing, eval gates, multi-LoRA serving.
ClassicMedium121. Design an LLM Evaluation Platform
GPU batch generation behind a shared cache, sandboxed pass@k, calibrated judges, paired statistics, release gates, contamination checks.
ClassicMedium126. Design Model Distillation at Scale
A 70B teacher distilled into an 8B student: transfer sets, logit and on-policy distillation, per-slice gates, escalation routing, a refresh loop.
ClassicMedium127. Design a Model Quantization and Compression Pipeline
Every new LLM version shrunk to FP8, INT8 or INT4 with a smaller KV cache, gated against its BF16 parent and benchmarked on the serving GPU.
ClassicMedium128. Design a Synthetic Data Generation Pipeline
Teacher models write training data for smaller students: conditioned and evolved prompts, verified answers, dedup, decontamination, versioned datasets with lineage.
ClassicMedium