The Fast Path20/20 - Expert Parallelism: Routing Tokens Through a City of SpecialistsJune 30, 202619/20 - Prefill-Decode Disaggregation: Two Worker Pools, One Token StreamJune 28, 202618/20 - Chunked Prefill: How to Stop One Long Prompt from Freezing Everyone ElseJune 27, 202617/20 - Continuous Batching: The GPU Schedule That Never Stands StillJune 26, 202616/20 - Streaming Generation: The First Token Is a Product DecisionJune 25, 202615/20 - Memory Offloading: Trading Bandwidth for CapacityJune 24, 202614/20 - Dynamic Batching: Waiting Microseconds to Save MillisecondsJune 23, 202613/20 - Graph Optimization: Teaching ONNX and TensorRT to See the Whole ModelJune 22, 202612/20 - Sequence Parallelism: Divide the Tokens, Not the MeaningJune 21, 202611/20 - Pipeline Parallelism: Turning Model Depth into an Assembly LineJune 20, 202610/20 - Tensor Parallelism: Splitting One Layer Across Many GPUsJune 19, 20269/20 - Quantized Kernels: Why a 4-Bit Model Is Not Automatically FastJune 18, 20268/20 - Mixed Precision Inference: Spend Bits Where They MatterJune 17, 20267/20 - Parallel Decoding: Predicting More Than One Future at a TimeJune 16, 20266/20 - Early Exit Decoding: Stop Computing Once the Answer Is ClearJune 15, 20265/20 - Batch Inference: When Throughput Matters More Than ImmediacyJune 14, 20264/20 - PagedAttention: Virtual Memory for the KV CacheJune 13, 20263/20 - FlashAttention: Why Moving Fewer Bytes Beats Doing Fewer FLOPsJune 12, 20262/20 - Speculative Decoding: Let a Small Model Guess, Let a Large Model JudgeJune 11, 20261/20 - KV Caching: The Memory That Makes Token Generation PossibleJune 10, 2026