Gpu-Memory

Optimizing vLLM's PagedAttention Allocator for High-Throughput Batched Inference

A working engineer’s guide to the internals of vLLM’s PagedAttention memory allocator, with concrete knobs, profile traces, and production patterns for squeezing more tokens per second out of a single GPU.

Stylized illustration of stacked HBM dies next to a GPU die on an interposer.

GPU Memory in 2026: HBM, KV-Cache, and the Real Bottlenecks Behind LLM Inference

A working engineer’s guide to GPU memory: HBM bandwidth, KV-cache math, fragmentation, and the production patterns that keep large model inference honest.

RAM vs VRAM: A Deep Dive for Large Language Model Training and Inference

Introduction In the world of large language models (LLMs), memory is a critical bottleneck. RAM (system memory) and VRAM (video RAM on GPUs) serve distinct yet interconnected roles in training and running models like GPT or Llama. While RAM handles general computing tasks, VRAM is optimized for the massive parallel computations required by LLMs.[1][3][4] This detailed guide breaks down their differences, impacts on LLM workflows, and optimization strategies, drawing from hardware fundamentals and real-world AI applications. ...