A practical guide to how attention actually works — scaled dot-product, multi-head, MQA, GQA, Flash Attention, and RoPE — with the implications for memory, throughput, and context length that matter for production deployments.
Handwritten test cases give false confidence. Building an eval dataset from production logs — with stratified sampling, proper labeling, and slice-based reporting — produces evaluations that actually catch regressions before they reach users.
MLflow, W&B, and Neptune all track experiments but optimize for different teams and workflows. A practical comparison across UI quality, self-hosting, hyperparameter optimization, and pricing — with a clear decision framework.
CUDA out of memory errors are almost always solvable without buying more hardware. A practical checklist of GPU memory reduction techniques — gradient checkpointing, 8-bit Adam, LoRA, Flash Attention, and more — in the order to try them.
LoRA, QLoRA, and full fine-tuning each solve the adaptation problem differently. A practical breakdown of memory costs, quality trade-offs, and when each approach is actually the right choice for your hardware and task.
DDP, FSDP, and DeepSpeed ZeRO all distribute training across GPUs but solve different problems. A practical breakdown of when to use each, with memory calculations and a clear decision framework for 7B to 70B model training.
torch.compile delivers 20–50% inference speedup with a single line of code. A practical guide to compilation modes, handling dynamic shapes, combining with Flash Attention and quantization, and knowing when not to use it.
A practical comparison of vLLM, HuggingFace TGI, and NVIDIA Triton Inference Server for production LLM deployment — covering throughput, latency, quantization support, multi-GPU serving, and when to use each.
Large language models like GPT-4 and Claude are impressive, but they don’t know anything about your company’s internal documents, your personal notes, or your proprietary data. Building a private AI assistant that can actually answer questions based on your specific information requires combining a local LLM with retrieval-augmented generation (RAG). This guide walks you through … Read more
Running large language models locally has evolved beyond simple inference tools into sophisticated platforms optimized for different workloads. Three solutions dominate the landscape: Ollama for simplicity and developer integration, vLLM for production-grade serving at scale, and Text Generation WebUI (oobabooga) for maximum control and experimentation. Each targets fundamentally different use cases, and choosing the wrong … Read more
