Deep technical guides on Flash Attention, LLM fine-tuning, GPU optimization, distributed training, and modern ML infrastructure.
Compare Hugging Face Accelerate and PyTorch Lightning for distributed training. Learn the differences in philosophy, features, and when to use each framework.
Essential CUDA knowledge for ML engineers. Learn GPU architecture, memory hierarchy, kernel optimization, and how to profile and optimize PyTorch code for maximum performance.
Complete guide to distributed training in PyTorch. Learn DDP, FSDP, and DeepSpeed ZeRO with practical examples, memory analysis, and scaling strategies for training models from 1B to 100B+ parameters.
Step-by-step tutorial for fine-tuning Qwen3 models including dense and MoE variants. Includes complete code examples, thinking mode configuration, dataset preparation, and deployment tips.
Comprehensive guide to LLM fine-tuning covering full fine-tuning, LoRA, QLoRA, data preparation, hyperparameters, and evaluation. Includes code examples for LLaMA, Mistral, and other models.
Detailed comparison of FlashAttention-2 and FlashAttention-3. Covers Hopper optimizations, FP8 support, performance gains, and migration considerations for H100 users.
Deep dive into Flash Attention's IO-aware algorithm, memory hierarchy optimization, and why it delivers 2-4x speedups. Covers FlashAttention-1, 2, and 3 with benchmarks and implementation details.
Head-to-head comparison of Flash Attention and standard PyTorch attention. Includes benchmarks, memory usage analysis, and guidance on when each approach wins.
Head-to-head comparison of PyTorch FSDP and DeepSpeed ZeRO. Covers performance benchmarks, feature differences, and guidance on when to use each for distributed LLM training.
Master GPU memory optimization for training large models. Covers memory anatomy, OOM debugging, gradient checkpointing, mixed precision, and advanced techniques with practical PyTorch examples.
Deep dive into gradient checkpointing for training large models. Learn how it works, when to use it, and implementation details with PyTorch code examples.
Compare the three main approaches to aligning LLMs: instruction tuning, RLHF, and DPO. Learn when to use each method with practical implementation guidance and real benchmarks.
Deep dive into the KV cache mechanism in transformers. Learn how it works, memory requirements, optimization techniques like MQA/GQA, and paged attention implementations.
Complete guide to optimizing LLM inference for production. Covers KV caching, quantization, batching strategies, speculative decoding, and serving frameworks with benchmarks.
Complete comparison of LLM quantization methods. Learn how GPTQ, AWQ, and GGUF work, their quality-speed trade-offs, and when to use each for production deployment.
In-depth comparison of fine-tuning methods for LLMs. Covers memory requirements, performance trade-offs, and when to use each approach with practical benchmarks.
Complete guide to mixed precision training in PyTorch. Learn the differences between FP16 and BF16, when to use each, and how to implement stable training with code examples.
Step-by-step guide to setting up multi-GPU training infrastructure. Covers hardware selection, networking, NCCL configuration, and troubleshooting for distributed PyTorch training.
Complete guide to torch.compile in PyTorch 2.0. Learn how it works, when to use it, common pitfalls, and benchmarks showing real-world speedups for training and inference.
Complete guide to transformer attention mechanisms. Learn scaled dot-product attention, multi-head attention, and modern variants like MQA and GQA with visual explanations and PyTorch code.