#Vllm

Meta Description: Compare vLLM, SGLang, TensorRT-LLM, LMDeploy, and llama.cpp to choose the right inference server for production LLMs and AI agents.

I stood up Google's diffusion language model DiffusionGemma as a vLLM OpenAI-compatible server on my home RTX 5090 (Blackwell / 32GB), so Open WebUI could use it as just another model. Declarative, on-demand on NixOS.…

CVE-2026-114055: Uncontrolled Resource Consumption in vLLM via Unvalidated Multimodal...

vLLM KV cache sizing explained: bytes per token, why a 24GB GPU runs 4 RAG requests at once, and the flags that raise concurrency.

If you serve LLM agents in production, you have probably seen this week's numbers: in a multi-turn...

Contribute to ryanzhou/deepseek-v4-flash-mi300x development by creating an account on GitHub.

LocalAI is the open source AI engine. Run any model, LLMs, vision, voice, image and video, on any hardware. No GPU required.

Contribute to kyuz0/amd-strix-halo-vllm-toolboxes development by creating an account on GitHub.

A deep dive into memory fragmentation, paged memory management, and why PagedAttention can deliver up...

KVarN is a native vLLM KV-cache quantization backend for your agents: 3-5x more context, throughput above FP16, and FP16-level accuracy. Calibration-free, one flag. - huawei-csl/KVarN

Learn how to self-host Langfuse, connect it to vLLM, and build full LLM observability with traces, tokens, latency, and dashboards — from scratch.

From Dev.to - tutorial: How to Deploy Mistral Nemo with vLLM + Flash Attention on a $12/Month DigitalOcean GPU Droplet: 3x Faster Inference at 1/95th Claude Cost

From Dev.to - ai: How to Deploy Llama 3.2 with vLLM + Batch Processing on a $8/Month DigitalOcean Droplet: Asynchronous Inference at 1/125th Claude Cost

Inside vLLM's CPU backend: a new contributor's notes Most of the public technical writing...

Deploy LLMs in production with vLLM, Hugging Face TGI, and ONNX Runtime. Learn quantization techniques, GPU memory optimization, and serving strategies.

From Dev.to - tutorial: How to Deploy Llama 3.2 90B with vLLM + Speculative Decoding on a $16/Month DigitalOcean GPU Droplet: 2.5x Faster Inference at 1/110th Claude Cost

From Dev.to - webdev: How to Deploy Llama 3.2 70B with vLLM + Quantization on a $12/Month DigitalOcean GPU Droplet: Enterprise Inference at 1/110th Claude Cost

From Dev.to - machinelearning: Counterintuitive: WSL2 + vllm cannot fit Qwen2.5-7B-1M on 6GB VRAM where Windows transformers can