High-Level Overview
Training Optimizations
Reducing Latency
Mixed Precision Training: Reduces memory usage and speeds computation.
Optimizer Enhancements (e.g., AdamW, LAMB): Accelerates convergence.
FlashAttention: Optimizes attention computation for speed and memory.
Cocktail SGD: Reduces network overhead in distributed training.
Sub-Quadratic Architectures (e.g., Striped Hyena): Lowers computational complexity.
Higher Throughput
Gradient Accumulation: Enables large batch sizes on limited GPU memory.
Data Parallelism: Splits data across GPUs for faster training.
Model Parallelism: Distributes model layers across GPUs.
Pipeline Parallelism: Pipelines model layers across GPUs for efficiency.
Gradient Checkpointing: Trades computation for memory savings.
LoRA Optimization: Efficient fine-tuning for large models.
Inference Optimizations
Reducing Latency
Layer Fusion: Combines operations into single kernels.
Speculative Decoding: Uses draft models to predict tokens faster.
SplitRPC: Splits control/data paths for reduced latency.
FlashAttention-3: Enhances inference speed for long sequences.
Custom CUDA Kernels: Optimizes specific ops (e.g., softmax).
Higher Throughput
Quantization: Reduces precision (e.g., FP16, INT8) for faster inference.
Dynamic Batching: Groups requests for GPU efficiency.
Continuous Batching: Processes requests as they arrive.
Caching (e.g., KV Cache): Reuses computed values.
Knowledge Distillation: Trains smaller, faster models.