Intel Inference Microservices bring tuned LLM inference to Xeon processors through containers, with no manual hardware optimization needed.

#IntelInferenceMicroservices #LLMInference #IntelXeon #vLLM #Kubernetes #Docker #CrescentIsland

vLLM Quickstart: High-Performance LLM Serving - in 2026

#LLM #AI #Python #Docker #DevOps #SelfHosting #vllm #K8S

https://www.glukhov.org/llm-hosting/vllm/vllm-quickstart/

GraphTok moves tokenization from CPU to GPU, running pre-tokenization through BPE as a single replayable CUDA graph that matches reference token IDs across major open models and integrates with vLLM and NVIDIA Dynamo. The design…

#GPUs #AIInfrastructure #LLMs #vLLM
https://arxiv.org/abs/2610.13325

This guide covers a dedicated Ubuntu GPU server with vLLM + Docker: model sizing, NVIDIA setup, text and image tests, and a systemd service that restarts itself.

www.fitservers.com/tutorials/ho...

#Gemma3 #vLLM #SelfHosted #GPU

Any locally running #LLM to #system-1 #decisions #API. #inference Backends: #Huggingface, #llama.cpp, #vLLM, #SGLang. #Jev
#OpenSource Apache 2.0 lic

Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA

Allison Li, Kristjan Greenewald, Thomas Parnell, Navid Azizan

Action editor: Zachary Charles

https://openreview.net/forum?id=Q8nCBmOkyn

#pipelines #vllm #cache

Running vLLM natively on Windows 10/11 with CUDA and an OpenAI-compatible server, no WSL or Docker, via the open-source vllm-windows-build patches and wheels. #vllm

Host your own lightning-fast AI server! This guide covers setting up vLLM to run LLM efficiently with minimal GPU memory.

If you want to see the coding part, view the full tutorial on our website! 👇
🔗 www.ctcservers.com/tutorials/ho...

#vLLM #AI #LLMs #MachineLearning #SelfHosted #Python

TriCalRAG is a reproducible benchmark for on-premise log-anomaly detection and root-cause analysis, evaluating Qwen2.5-14B and Mistral-Small-22B on vLLM with one NVIDIA RTX PRO 6000 GPU across BGL, HDFS, Thunderbird, and OpenStack. It…

#TriCalRAG #AIOps #RAG #vLLM
https://arxiv.org/abs/2609.14762

Want to learn more about self-hosting your own models and agents? This Thursday at 10am ET I’m joining @hacktoberfest.com for some live demos and tips on using #vLLM 🎃

Bring your questions, I'll try to get to as many as I can :)

🔧 Runs on #vLLM with Aleph Alpha's inference plugin, exposing an OpenAI-compatible API. Needs 2x A100/H100 80 GB or 1x H200/B200/B300. Best fit: on-prem #RAG over long German documents like laws, contracts and manuals

A serving setup for Qwen3.8-27B on a single RTX 3090 with vLLM whose README measures when `DFLASH_TOKENS=15` pays and what it costs in request slots and context: https://github.com/syv-ai/qwen38-27b-rtx3090

#Vllm #LocalLlm #SpeculativeDecoding

vLLM Scheduling: throughput vs latency in shared inference.

The GPU is shared; what decides how many fit is memory. Prefill, chunked prefill, KV cache.

📖 https://devopstales.github.io/ai/vllm-scheduling-throughput-latency/?utm_source=bluesky&utm_medium=social
#vllm #LLM #inference

https://lckhd.eu/4QPawG

#vLLM #Kubernetes #GPU #MLOps

Many teams want to host AI models in their own Kubernetes clusters to control spend and access. The usual Kubernetes answer to growing demand is more replicas, bigger nodes, and an autoscaler. With GPUs isn't always the best approach because

Alibaba's Qwen3.8-2.4T-A95B model is now open weights, ready for the most demanding tasks. Deploy on Amazon SageMaker HyperPod using vLLM for high performance and control. #Alibaba #Qwen38 #AmazonSageMaker #vLLM

New arXiv work introduces DLFP, a model-free vLLM controller that resizes prefill chunks based on observed decode-latency feedback to cut interference during concurrent inference on a single A100. Strong Qwen3-0.6B BF16 results, though…

#AI #LLMInference #vLLM #GPU
https://arxiv.org/abs/2609.38386

AI-discovered flaws are getting weaponized fast. Google Threat Intelligence Group says disclosures doubled in 2026, yet real-world exploitation stayed rare. Attackers are also targeting AI tools like Flowise and vLLM. #BeyondTrust #AI Security #vLLM

✍️ New blog post by xbill

Gemma 4 on Amazon SageMaker or a VM? The Same Model Server at 1.40x the Price

#aws #sagemaker #gemma #vllm

✍️ New blog post by xbill

Gemma 4 on Amazon SageMaker: The NVIDIA T4 Decodes at 0.8x of the L4 With the Same Answers

#aws #sagemaker #gemma #vllm

✍️ New blog post by xbill

Gemma 4 on Amazon SageMaker: 4-Bit Embeddings Decode up to 1.39x Faster on One L4

#aws #sagemaker #gemma #vllm