#vllm

[cyber-sec]

Critical vLLM Vulnerability Lets Attackers Execute Code via Video Links

vLLM CVE-2026-22778 allows remote code execution through malicious video URLs exploiting a JPEG2000 heap overflow and PIL information leak.

Source: OX Security

Read more: CyberSecBrief

[dasroot]

Learn how to deploy vLLM at scale on Kubernetes with PagedAttention, continuous batching, and tensor parallelism for high-throughput LLM inference. Covers multi-GPU, multi-node strategies and best practices.

[rostglukhov]

Choosing the best way to run LLMs locally? Compare Ollama, vLLM, TGI, SGLang, LM Studio, LocalAI and 8+ tools by API support, hardware compatibility, tool calling, and production readiness.

[servermo]

The Engineering Truth of vLLM Multi-GPU Inference

Stop guessing why your LLM is slow. Master NVLink aggregate bandwidth, prefix caching, NVMe loading bottlenecks, and honest bare metal ROI.

Your $30,000 H100 GPU is probably thermal throttling right now.

Let’s cut through the AI marketing hype. Buying enterprise GPUs is only half the battle. If your datacenter fundamentals are flawed, your massive LLM infrastructure is just a highly expensive space heater.

Here is the actual engineering truth behind LLM serving:

The NVLink Reality Check

Most tutorials scream "PCIe is dead for AI!" That is a massive overstatement. If you are running 7B/13B models with Data Parallelism, PCIe Gen 5 is perfectly fine. The narrative only changes for 70B+ models requiring Tensor Parallelism (TP), where the heavy AllReduce synchronization overhead makes NVLink scaling efficiency completely crush PCIe.

The Thermal & Storage Bottleneck

An H100 draws 700W+. If your server lacks liquid cooling or proper high-CFM datacenter fans, the GPU will silently protect itself by downclocking. Your vLLM performance will unpredictably degrade after 10 minutes of heavy load. Furthermore, standard SSDs will make loading a 140GB model into VRAM take agonizing minutes. You need PCIe Gen 5 NVMe.

Software isn't Magic (vLLM Tuning)

vLLM's PagedAttention is brilliant, but it isn’t a magic "3x concurrency" button. True production speed requires dropping defaults. You need to configure --ipc=host for fast shared-memory IPC, leverage FP8 to cut VRAM requirements, and enable --swap-space to offload KV cache overflows to CPU RAM instead of crashing with an OOM error.

The Cloud "Virtualization Tax"

Cloud VMs are great for startups, but at scale, you pay massive API taxes and suffer from virtualization latency jitter. If you are running sustained 24/7 production workloads or AI Gaming engines, you need the raw, unshared power of Dedicated Bare Metal.

Stop falling for marketing specs. Master your hardware.

📖 Read our full vLLM & NVLink Engineering Blueprint here: 🔗 https://www.servermo.com/howto/vllm-multi-gpu-setup/

[rostglukhov]

Choosing the best way to run LLMs locally? Compare Ollama, vLLM, LM Studio, LocalAI and 8+ tools by API support, hardware compatibility, tool calling, and production readiness.

[cyberdudebivash-news]

2026 Emerging Threats: "AI-Orchestrated Attacks" - CYBERDUDEBIVASH REPORT

Read the full report on -

2026 Emerging Threats: "AI-Orchestrated Attacks" - CYBERDUDEBIVASH REPORT

CyberDudeBivash News delivers daily cybersecurity threat intel, CVE alerts, malware trends, and crypto security briefings.

[rostglukhov]

vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs) developed by UC Berkeley’s Sky Computing Lab.

With its revolutionary PagedAttention algorithm, vLLM achieves 14-24x higher throughput than traditional serving methods, making it the go-to choice for production LLM deployments.

What is vLLM?

vLLM (virtual LLM) is an open-source library for fast LLM inference and serving that has quickly become the industry standard for production deployments. Released in 2023, it introduced PagedAttention, a groundbreaking memory management technique that dramatically improves serving efficiency.

[rostglukhov]

LLM Context Length vs VRAM: Optimization Strategies Comparison

Compare vLLM and LM Studio for optimizing LLM context length and VRAM usage in 2026. Learn how KV cache management, quantization, and parall

LLM context length and VRAM management strategies are compared in the context of optimizing long document processing during LLM inference in 2026.

This analysis evaluates how different frameworks handle context length limitations and VRAM constraints through architectural and implementation choices. Key differences include performance under high context loads, memory efficiency, and support for specific optimization techniques. The comparison covers vLLM 0.6 and LM Studio 2.1 across various hardware configurations.