mljourney 
Most RAG failures are retrieval failures. A practical guide to the techniques that actually move the needle: hybrid BM25 + dense retrieval, reciprocal rank fusion, cross-encoder reranking, HyDE query transformation, and chunking strategies — with retrieval evaluation built in.
Tensor parallelism and pipeline parallelism split models across GPUs in fundamentally different ways with different latency and throughput implications. A practical guide covering how each works, NVLink vs InfiniBand trade-offs, and how they combine for 70B+ multi-node serving.
Off-the-shelf embedding models underperform on domain-specific retrieval. A practical guide to synthetic query generation, hard negative mining, contrastive fine-tuning, and Matryoshka training — with realistic expectations on NDCG@10 improvement.
AWQ, GPTQ, and bitsandbytes each quantize LLM weights differently. A practical comparison covering how each works, quality at 4-bit and 8-bit, inference speed, and when to use which — including the right choice for QLoRA training vs production serving.
FlashAttention-2 delivers 2–4x faster attention and eliminates O(N²) memory with a single argument change. A practical guide to enabling it in HuggingFace, PyTorch SDPA, and fine-tuning pipelines — including attention mask compatibility and where it helps most.
RLHF with PPO is powerful but complex. DPO achieves comparable alignment with a fraction of the engineering overhead. A practical comparison of PPO, DPO, and newer variants like SimPO and ORPO — covering when to use each and how to build a preference dataset that actually works.
Serving multiple fine-tuned model variants with separate deployments wastes GPU memory proportional to the number of adapters. A guide to multi-adapter serving with vLLM, S-LoRA architecture, adapter routing strategies, and GPU memory planning.
Feast, Tecton, and Hopsworks each take a different approach to feature store architecture. A practical comparison covering offline and online stores, streaming feature support, managed vs self-hosted trade-offs, and how to choose based on your actual requirements.
GPU sitting at 40-60% utilization while the model code looks fine? The dataloader is likely the bottleneck. A systematic guide to diagnosing and fixing slow data loading in PyTorch training pipelines.
Gradient accumulation and gradient checkpointing are frequently confused but solve different problems. A precise guide to how each works, when to use them, how to combine them, and how to reason about GPU memory during training.
