Artificial Intelligence
Deep dives into modern artificial intelligence, neural architectures, large language models, agentic workflows, and machine learning breakthroughs.
Disaggregated LLM Serving: Architecting Asynchronous Prefill-Decode Engines with Zero-Copy RDMA KV-Cache Migration
Learn how Disaggregated LLM Serving decouples prefill and decode using zero-copy RDMA KV-cache migration across GPU clusters to eliminate ITL jitter.
Architecting Multi-Tier Disaggregated KV-Cache Hierarchies for Terabyte-Scale LLM Inference
Learn how to build a Multi-Tier Disaggregated KV-Cache Hierarchy using CXL pooled memory, host DRAM, NVMe-oF, asynchronous kernels, and tiered eviction.
Architecting High-Performance Onchain AI Agents: Data Integrity, Hybrid Reasoning, and Execution Guardrails
Learn how to build onchain AI agents using point-in-time state reconciliation, stateful multi-agent graphs, and automated execution circuit breakers.
How FP8 Quantization Works in Modern Inference Engines
Learn how FP8 quantization works in LLM inference, including E4M3 and E5M2 data formats, scaling granularities, and asynchronous tensor core datapaths.
Mixture-of-Depths: Architectural Foundations, Routing Dynamics, and Variable Token Compute in Large Language Models
Learn how Mixture-of-Depths uses learned routing scalars, dynamic capacity budgets, and residual identity paths to allocate variable compute across tokens.
How Speculative Decoding in LLM Serving Actually Works
Explore how speculative decoding in LLM serving accelerates token generation via draft models, tree attention verification, Triton kernels, and math bounds.