A technical deep dive explains how vLLM's PagedAttention and continuous batching address the memory fragmentation and GPU starvation that bottleneck production LLM inference. PagedAttention partitions…
A developer detailed the memory and GPU VRAM bandwidth bottlenecks that dominate LLM inference engineering, focusing on the KV-Cache's linear growth with context length and batch size. The writeup exp…
A technical explainer details how KV cache memory, not model weights, dominates VRAM during LLM inference: for a 13-billion-parameter model with 40 layers, 40 KV heads and 128-dimension heads at 16-bi…
VLLM is an open-source large language model serving engine built around PagedAttention, which manages the KV cache the way an operating system manages virtual memory, and continuous batching, which ke…
VLLM's PagedAttention and continuous iteration-level batching address the KV cache memory bottleneck that limits LLM inference throughput, according to a technical deep dive on the inference engine's …
An engineer at g factor detailed how vLLM's PagedAttention and continuous iteration-level batching solve the memory-bandwidth bottleneck in production LLM inference, drawing on benchmarks run on dedic…
A 2026 developer guide compares four local LLM inference engines for workstations and homelabs, highlighting Ollama for turnkey developer ergonomics and agent backends, vLLM for high-throughput batch …
A developer's source-code walkthrough of vLLM 0.22's V1 execution path traces a single offline inference request from the LLM.generate() API through inter-process communication, scheduling, GPU execut…
Researchers introduced GrowPage, an on-demand KV budgeting framework for efficient LLM reasoning serving, which dynamically adjusts key-value cache capacity per request based on attention demand. Grow…
VLLM's PagedAttention technique reduces GPU memory waste in LLM serving from 60-80% to less than 4% by partitioning the KV cache into non-contiguous blocks, according to a technical analysis. The meth…
Intel Gaudi 3's vLLM-Gaudi PagedAttention implementation underutilizes hardware for sliding window attention, but optimizations for serving Gemma 4 31B expanded usable KV cache by 3.5×, raised decode …
A developer's production guide details how to self-host vLLM on cloud GPUs to achieve sub-180ms LLM inference for autonomous AI agents, cutting costs by 45-74%. The setup leverages vLLM v0.6+, EAGLE-3…
Maneshwar, developer of the git-lrc AI code reviewer, explains that the perceived speed differences between LLM chatbots like ChatGPT and Gemini stem largely from the inference engine, not the model's…
A developer's guide separates the local-versus-cloud GPU debate into four practical questions, emphasizing that price alone cannot determine the best deployment option. The article highlights that dep…
Neon AI Gateway, powered by Databricks Foundation Model APIs, achieves fast open-weight model inference through optimizations like continuous batching, KV-cache paging, and prompt caching, which boost…
A developer's guide explains that tokens per second (tok/s) benchmarks for local LLMs can be misleading, as the same model can show vastly different speeds depending on concurrency. The article highli…
VLLM's KV cache and PagedAttention techniques reduce latency and cost in large language model inference by storing and efficiently managing key-value matrices, enabling higher throughput on existing G…
VLLM outperforms Ollama by nearly 20x in throughput at high concurrency, according to benchmark tests running Llama 3.1 8B on an NVIDIA A100 40GB, with vLLM peaking at 793 tokens per second versus Oll…
Context management is becoming a critical infrastructure layer for production AI systems, with KV-cache optimization and contradiction-aware retrieval emerging as two distinct but connected challenges…
HexCore, a low-latency paged KV cache allocator for LLM inference written in C++20 and CUDA, has been released under the Apache License 2.0 by Rasuljanov Muhammadali. The CPU-side allocator and relate…