A deep dive into vLLM’s tiered model integration — from the Transformers fallback that enables zero-day support to the native integration path that makes it fast.
Orchestrating Inference: How Kubernetes, Ray, and vLLM Coordinate Under the Hood
A deep dive into how Kubernetes, Ray, and vLLM coordinate to transform independent GPUs into a synchronized inference machine.
The Hidden Software Stack Behind Fast LLM Inference
Beyond vLLM and PagedAttention: exploring NCCL, CUTLASS, Triton, and FlashInfer, the libraries that actually make LLM inference fast.
Speculative Decoding: When Guessing Right Makes for Faster Inference
How speculative decoding achieves 2-3× inference speedup without changing model outputs, and why GLM-4.7’s native multi-token prediction marks a paradigm shift.
The Anatomy of Agentic Code Assist: Building Production Grade AI Coding Agents
A deep dive into the architecture, design patterns, and engineering decisions behind production-grade agentic code assist solutions. By dissecting OpenHands, we uncover how to build AI agents that safely execute code, manage complex state, and operate reliably in production.
QuIP#: Achieving Near-Lossless 2-Bit LLM Quantization
QUIP# algorithm for quantizing LLM weights without gradient information.
Why Can Your Laptop Run LLaMA? A Deep Dive into Quantization
How 4–8x compression and Hessian-guided GPTQ make 70B-scale models practical on modest hardware—what INT8/INT4 really cost, and when accuracy holds.
Flash Attention: The Mathematical Tricks That Broke the Memory Wall
Flash Attention, a memory-efficient attention mechanism for transformers.
Advanced NVIDIA GPU Monitoring for LLM Inference: A Deep Dive into H100 Architecture and Performance Optimization
A deep dive into NVIDIA’s H100 architecture and the monitoring techniques required for production-grade LLM inference optimization.
Beyond Prefix Caching: How LMCache Turns KV Cache into Composable LEGO Blocks
How LMCache Turns KV Cache into Composable LEGO Blocks