Overview
Scaling large language models requires understanding hardware-level acceleration, distributed computing, and specialized deep learning libraries. Key areas include GPU/TPU architectures, fused CUDA kernels, quantization-aware training, and distributed training frameworks like DeepSpeed and Megatron-DeepSpeed.Tools & Libraries
- Distributed Computing & Scheduling: Ray, MPI4Py, Joblib
- Deep Learning & Model Acceleration: DeepSpeed, Hugging Face Accelerate
- Experiment Tracking & Data Management: MLflow, DVC
- GPU & CUDA Resources: NVIDIA GPU Operator, CUDA C Programming Guide, Numba CUDA Kernels, NCCL
- Additional Learning Resources: Sakana AI CUDA Engineer, Modal GPU Glossary, Custom CUDA Kernels Guide
Key Concepts
- Hardware accelerators: NVIDIA A100/H800, NVLink, NVSwitch, InfiniBand
- Fused CUDA kernels & FlashAttention implementations
- Quantization-aware training
- Collective Communication (NCCL)
- Parallelism strategies: Data, Model, Pipeline
- Mixture-of-Experts (MoE), Multi-Token Prediction (MTP), Multi-head Latent Attention (MLA)
- DeepSpeed ZeRO optimizations
- Advanced model scaling: Megatron-DeepSpeed, Hugging Face scaling papers
Tutorials & Case Studies
- DeepSpeed Megatron Tutorial
- JAX Scaling Book
- Ultrascale Playbook
- Meta LLM Training Infrastructure
- PyTorch Distributed Overview
- Hugging Face Blogs on DeepSpeed & Megatron
- NVIDIA & Alpa scaling guides, Microsoft Research blogs, and relevant arXiv papers
Prerequisites & Courses
Math & Algorithms
- Linear algebra, probability, optimization (SGD variants)
- Complexity analysis, communication vs computation costs
Computer Architecture
- CPUs vs GPUs vs TPUs, memory hierarchies, SIMD, NVLink
- NUMA fundamentals
Parallel Computing Paradigms
- Shared-memory: OpenMP, pthreads
- Distributed-memory: MPI, RPC
- Data, model, and pipeline parallelism
Recommended Courses
- High-Performance Parallel Computing Specialization (Coursera)
- GPU Programming Specialization (Coursera)
- Stanford GPU Programming YouTube Playlist
- NVIDIA Developer GPU Programming Tutorials
Learning Plan
- Start with math, algorithms, and parallel computing fundamentals.
- Explore GPU architectures, CUDA, and hardware accelerators.
- Practice distributed computing using MPI, Ray, and DeepSpeed.
- Study tutorials and blogs for large-scale LLM training workflows.
- Implement small-scale experiments, scaling gradually to multi-GPU and cluster setups.
- Track experiments and data efficiently with MLflow or DVC.
- Review advanced papers to understand state-of-the-art techniques like MoE, FlashAttention, and ZeRO optimization.