A source-audited study of over-training, downstream metric behavior, lifecycle cost, measured serving demand, uncertainty-aware artifact selection, pilot gates, and a training authorization that hands release and rollback requirements to the serving and deployment modules.
A source-audited study of neural scaling experiments: measurement contracts, power laws, joint model-data loss surfaces, compute-optimal allocation, paper-contract comparison, fitting, and uncertainty.
A tensor-to-cluster study of MoE execution: logical and physical experts, variable-size dispatch, all-to-all communication, grouped and block-sparse kernels, prefill/decode modes, expert replication, topology, overlap, precision, observability, and failure recovery.
A derivation- and implementation-oriented study of Transformer MLPs: position-wise channel mixing, activation and GLU variants, SwiGLU width matching, backward flow, fused execution, knowledge interpretations, and memory alternatives.
A systems study of serving many LoRA adapters over one base model: heterogeneous batching, segmented kernels, paging, residency, merge and hotswap semantics, request/KV compatibility, scheduling, observability, and failure recovery.
A distribution- and serving-state-oriented study note on speculative decoding, draft models, target verification, exact speculative sampling, correction distributions, acceptance rate, speedup ledgers, and KV rollback.
A serving-oriented study note on KV cache lifecycle, prefill, decode, TTFT, TPOT, memory admission, and why autoregressive inference turns attention state into a scheduler problem.
A serving-scheduler study note on static batching failure, iteration-level scheduling, continuous batching, chunked prefill, TTFT, TPOT, goodput, and KV-aware admission control.
A code- and shape-oriented study note on Transformer cost: parameters, activations, FLOPs, memory traffic, prefill, decode, and why FLOPs alone do not predict runtime.
A derivation- and implementation-oriented study of one autoregressive request: prompt state, prefill/decode, last-row logits, token selection, KV append timing, positions, stopping, beam reorder, streaming, and cache equivalence.
A reading guide to Transformer System: what each module explains, the questions behind its articles, and where to find their main sources.