AI / LLM · PRO TIER

vLLMpro

vLLM is the highest-throughput open-source LLM inference engine — built around PagedAttention for memory efficiency. Offers OpenAI-compatible REST API, tensor parallelism for multi-GPU, and serves all major modern LLMs (Llama 3.x, Mistral, Qwen, DeepSeek, etc.) at 5-10× the throughput of vanilla HuggingFace.

Install via WHMCS → Visit github.com ↗

🤖 AI / LLM Min 16384 MB RAM Port 8000 (http) Tier pro

// What it is

A closer look.

vLLM is the highest-throughput open-source LLM inference engine — built around PagedAttention for memory efficiency. Offers OpenAI-compatible REST API, tensor parallelism for multi-GPU, and serves all major modern LLMs (Llama 3.x, Mistral, Qwen, DeepSeek, etc.) at 5-10× the throughput of vanilla HuggingFace.

Used inside Anthropic's, Bedrock's, and many production LLM platforms — vLLM is the canonical choice for production LLM serving.

// Use cases

What it's for.

Concrete scenarios where teams pick vLLM over the SaaS alternative.

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Production LLM API

serve Llama 3.x, Mistral, Qwen at scale

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OpenAI-compatible endpoints

drop-in replacement for OpenAI API in clients

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High-throughput batching

continuous batching for many parallel users

▣

Memory-efficient

PagedAttention enables larger batch sizes

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Tensor parallelism

split big models across multiple GPUs

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Embedding inference

serve embedding models too

// Who it's for

Built for these teams.

If your team profile matches one of these, vLLM is a strong fit out of the box.

Profile A

AI app developers

serving LLM in production

Profile B

Startups

building OpenAI-API replacement infrastructure

Profile C

Enterprises

running internal LLM for compliance / cost reasons

Profile D

AI agencies

offering LLM API to clients

Profile E

Hosting providers

selling LLM-as-a-service

// Differentiators

Why teams pick vLLM.

When evaluating self-hosted options for this category, here are the dimensions on which vLLM consistently lands above the alternatives.

✓Apache 2.0 — fully open
✓Highest throughput — in production LLM benchmarks
✓PagedAttention — = more efficient VRAM than competitors
✓OpenAI-compatible API — = trivial client integration
✓Active development — by UC Berkeley + Anyscale
✓Industry adoption — used in Bedrock, Anthropic infra, many startups
✓Tensor parallelism — scales to 70B+ models across 4-8 GPUs

// Integrations

Connects to.

The stack you'll plug vLLM into — services, protocols, and adjacent apps in the BluixApps catalog.

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OpenAI-compatible REST

/v1/models, /v1/chat/completions, /v1/completions, /v1/embeddings

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Pair with

OpenWebUI (UI), AnythingLLM, LangChain, LlamaIndex, LiteLLM (multi-model gateway)

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HF model auto-download

gated models need HF_TOKEN

▣

Quantization

AWQ, GPTQ, FP8, INT8

▦

Multi-GPU

--tensor-parallel-size N

▩

Multi-node

Ray cluster support for cross-node inference

// Adoption & deployment

Notable users & community

33k+ GitHub stars
UC Berkeley Sky Computing Lab + Anyscale
Used inside Bedrock, Anthropic, OpenAI competitor stacks
Active development with weekly releases
Production deployments at thousands of companies

What we ship

Docker (vllm/vllm-openai:latest)
Default model: meta-llama/Meta-Llama-3.1-8B-Instruct (configurable via /opt/vllm/.env)
Persistent volume: /opt/vllm/models (HF cache)
Port 8000 (standard OpenAI port)
--max-model-len 8192 default
Install report at /root/bluixapps/vllm.txt
Recommended model list by VRAM tier
Pairing suggestions (OpenWebUI, AnythingLLM, LiteLLM)
HF_TOKEN environment variable for gated models
GPU pre-flight check via bluixapps_ensure_nvidia_runtime
Backup hook covers model cache