What is vLLM?
A high-throughput LLM inference server using paged-attention memory management — the typical production runtime for self-hosted open-weight models.
Also known as
vLLM — explained.
vLLM is the open-source LLM inference server that has become the production default for self-hosted deployments of open-weight models. Its core technical contribution is PagedAttention — a memory management algorithm that treats the KV cache like virtual memory pages, allowing the server to pack more concurrent requests onto a single GPU without fragmentation. Practically, that translates into 2-10× the throughput of naive implementations on the same hardware. vLLM exposes an OpenAI-compatible API, which is the second reason for its dominance — existing client code written against OpenAI's chat completion endpoint typically works against vLLM with a base URL swap. Production deployments add: request batching, speculative decoding, multi-LoRA serving (multiple fine-tunes loaded simultaneously), and quantisation (AWQ, GPTQ, FP8) for memory-bound models. Alternatives in the same space include Hugging Face TGI, Ollama (better for local / single-user), TensorRT-LLM (NVIDIA-optimised), and SGLang. For most Zeour on-prem AI deployments vLLM is the recommended starting point.
Zeour solutions that operate on this layer.
Verticals where vllm is operationally critical.
Blog posts that go deeper on vllm.
Adjacent definitions to read next.
On-Premises AI
AI & ModelsOpen-weight large language models running on the operator's own hardware — no prompt, completion, or embedding ever leaves the perimeter.
Open-Weight LLM
AI & ModelsA large language model whose trained parameters (weights) are published openly — runnable on the operator's own hardware without API dependency.
Arabic Language Model
AI & ModelsAn open-weight or fine-tuned LLM that handles Modern Standard Arabic and major dialects with appropriate tokenisation efficiency and right-to-left rendering at the application layer.
Context Window
AI & ModelsThe maximum amount of text an LLM can process in a single request, measured in tokens — caps how much document context can be fed for RAG and long-form analysis.
Embeddings
AI & ModelsNumerical vector representations of text (or images, or audio) where semantically similar inputs land in similar regions of vector space — the substrate of semantic search and RAG.
Fine-Tuning
AI & ModelsAdapting a pre-trained LLM to your domain or task by continuing its training on a small, high-quality dataset — typically via LoRA or full SFT.
Large Language Model
AI & ModelsA neural network trained on internet-scale text that produces fluent generative output and powers most of what people call "AI" in 2026 — including on-premises sovereign deployments.
Llama (Meta)
AI & ModelsMeta's open-weight LLM family — Llama 3.x is the dominant open-weight base for enterprise on-prem deployments through 2025-2026.
Talk to a Zeour engineer.
A 30-minute scoping call to walk your operational profile against where vllm actually sits in your stack, then a fixed-fee Discovery price by the end of the call.