we launched the most comprehensive ai performance engineering repo in the world last week now we'll be posting every single resource this is Wafer's ai performance engineering series save this as your starting point. links in thread 🧵 part 1: "All About Transformer Inference" from How To Scale Your Model. - the authors cover the computations, memory traffic, and serving decisions behind transformer inference: - arithmetic intensity of linear layers and attention across prefill and decode. - kv cache sizing by layer count, kv heads, head dimension, sequence length, and precision. - the compute/hbm bandwidth crossover and how batch size and quantization shift it. - decode latency and throughput bounds from parameter bytes, kv bytes, and hardware bandwidth. - weight reuse through batching and diminishing throughput gains as kv traffic grows. - gqa, kv quantization, and PagedAttention, including the memory costs each addresses. - model sharding, kv placement, and collective communication overhead. - continuous batching, prefix caching, and disaggregated prefill/decode. the worked problems connect model dimensions to deployment decisions like memory capacity, workload distribution, and expected performance under the stated assumptions.