Higher throughput than naive serving — continuous batching and PagedAttention keep your GPUs busy.
Per-token API fees. Serve unlimited requests on GPUs you own or rent.
OpenAI-compatible API — point existing apps at your own endpoint with a URL change.
What self-hosted vLLM serving gives you
Move from a desktop model or a metered cloud API to production-grade inference you own. What vLLM unlocks:
High-throughput serving
Continuous batching and PagedAttention serve many concurrent users on the same GPU.
OpenAI-compatible API
A drop-in /v1 endpoint, so apps built for OpenAI switch over with a URL and key change.
Your data stays in
Prompts and outputs never leave your infrastructure — deploy in your own VPC or on-prem.
Predictable cost
Replace per-token bills with fixed GPU cost — the more you use it, the more you save.
Any open model
Serve Llama, Qwen, DeepSeek, Mistral and more, with quantization to fit your GPUs.
Scale on Kubernetes
Autoscaling, health checks and rolling model updates for real production traffic.
Ready to build private AI you control?
Book a free 30-minute strategy session. We’ll map the fastest, most private path to a self-hosted AI setup for your use case — no obligation.
Why hosted LLM APIs fall short at scale
A hosted API is easy to start with, but at production volume the per-token bill compounds every month, your prompts and outputs leave your network, and you inherit the vendor’s rate limits, model choices and latency. For steady, high-volume workloads — or anything confidential — you want to own the inference layer, not rent it.
How we deploy vLLM in your environment
We provision GPUs in your cloud or on-prem and deploy vLLM with the right open model and quantization (AWQ, GPTQ or FP8) so it fits your hardware and hits your latency targets. vLLM’s continuous batching and PagedAttention keep the GPUs saturated, and we expose an OpenAI-compatible /v1 endpoint behind authentication so your apps connect with a URL change.
From there we tune batching, context length, tensor and pipeline parallelism and throughput for your traffic, add observability, rate limiting and autoscaling on Kubernetes, and hand over a documented, self-hosted vLLM inference stack your team can operate — with no per-token meter and no prompts or data leaving your infrastructure.
Why teams choose vLLM for production inference
vLLM is the engine most teams reach for when a local model needs to serve real production traffic. Its two signature techniques — continuous batching and PagedAttention memory management — let a single GPU serve many concurrent requests at high throughput, which is exactly what a desktop tool like Ollama isn’t built for. If you’re moving from a prototype to an app that thousands of users or agents will hit, vLLM is the step up.
vLLM serves an OpenAI-compatible API, so anything already written against OpenAI points at your endpoint with a URL and key change. It runs the open models you already use — Llama, Qwen, DeepSeek and Mistral — with quantization (AWQ, GPTQ, FP8) to fit your GPUs, and scales across multiple GPUs and nodes with tensor and pipeline parallelism. Everything runs inside your own VPC or on-prem, so prompts and outputs never leave your network.
What vLLM gives you, and where we help:
- High-throughput serving — continuous batching and PagedAttention serve many concurrent users per GPU.
- OpenAI-compatible API — a drop-in /v1 endpoint so existing apps switch to your self-hosted vLLM with a URL change.
- Scales with your traffic — multi-GPU and multi-node serving with autoscaling on Kubernetes.
- Cost-efficient — quantized models and high GPU utilization replace per-token cloud bills with fixed capacity.
- Private by design — inference stays in your infrastructure; we add authentication, observability and rate limiting.
NeuralChainAI deploys and tunes vLLM, sizes the GPUs and quantization for your workload, wires the API into your apps, and hands you a documented, production-ready vLLM stack your team can run.
What we build with vLLM
OpenAI-compatible self-hosted API
A drop-in /v1 endpoint on your own infrastructure so apps built for OpenAI keep working, privately. Part of our self-hosted AI stack.
High-throughput model serving
Continuous batching and PagedAttention to serve many concurrent users per GPU. See our private & on-premise AI approach.
Migrate off metered cloud APIs
We move workloads from per-token cloud APIs to self-hosted inference, cutting cost and keeping data in-house. Explore private & on-premise AI.
Quantization, tuning & autoscaling
We quantize models to fit your GPUs, tune throughput and latency, and set up autoscaling for spiky traffic. Choosing an engine? Our guides compare vLLM vs SGLang vs TensorRT-LLM and cover deploying Llama, DeepSeek and Qwen in production.
Frequently asked questions
Ready to serve your own models in production?
Tell us your models, traffic and GPUs, and we’ll design a self-hosted inference stack that scales.