Self-Hosted LLM Inference and Serving, Built on vLLM
We deploy and tune vLLM to serve open models fast on your own GPUs — an OpenAI-compatible, self-hosted LLM API that handles real production traffic, with no per-token cloud fees and no data leaving your infrastructure. We design, deploy, tune and scale the whole stack.
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.
Self-host inference with vLLM.
Serve open models on your own GPUs behind an OpenAI-compatible API, keep every request inside your network, and trade a per-token meter for fixed, predictable capacity you control.
How We Deploy vLLM in Your Environment
From bare GPUs to a documented, production-ready inference stack your team can operate — with no per-token meter and no prompts or data leaving your infrastructure.
Provision & Deploy
We provision GPUs in your cloud or on-prem and deploy vLLM with the right open model and quantization (AWQ, GPTQ or FP8) to fit your hardware and hit your latency targets.
Expose the API
Continuous batching and PagedAttention keep the GPUs saturated. We expose an OpenAI-compatible /v1 endpoint behind authentication so your apps connect with a URL change.
Tune & Scale
We tune batching, context length, tensor and pipeline parallelism and throughput for your traffic, then add observability, rate limiting and autoscaling on Kubernetes.
Documented Handover
You get a documented, self-hosted vLLM inference stack your team can operate — no per-token meter, 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.
It serves an OpenAI-compatible API, runs the open models you already use — Llama, Qwen, DeepSeek and Mistral — with quantization (AWQ, GPTQ, FP8) to fit your GPUs, and scales across GPUs and nodes with tensor and pipeline parallelism. Everything runs inside your own VPC or on-prem.
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, sized to your latency and RPS targets.
Migrate Off Metered Cloud APIs
We move workloads from per-token cloud APIs to self-hosted inference, cutting cost and keeping data in-house.
Quantization, Tuning & Autoscaling
We quantize models to fit your GPUs, tune throughput and latency, and set up autoscaling for spiky traffic.
Frequently Asked Questions
vLLM is a high-performance inference engine for serving open large language models. It uses continuous batching and PagedAttention to serve many concurrent requests quickly on your own GPUs, and exposes an OpenAI-compatible API your applications can call.
Yes. We deploy vLLM inside your own cloud VPC or on-prem, so prompts and outputs never leave your infrastructure. You control authentication, logging and retention end to end.
Open-weight models such as Llama, Qwen, DeepSeek and Mistral, in the sizes and quantization that fit your GPUs. You can serve multiple models and update them without changing your API.
It replaces per-token API bills with fixed GPU cost, and its batching keeps GPUs highly utilized so you serve far more traffic per dollar. The higher your volume, the bigger the saving versus a metered cloud API.
Yes. vLLM serves an OpenAI-compatible /v1 endpoint, so most apps and libraries that already call OpenAI can point at your self-hosted endpoint with a URL and key change.
Yes — vLLM is built for production. It is widely used to serve open models at scale, and we deploy it with autoscaling, health checks, observability and rate limiting on Kubernetes so it handles real traffic reliably, with rolling model updates and no downtime.
Far more than a naive setup. Continuous batching and PagedAttention keep the GPU busy across many simultaneous requests, so a single GPU can serve high concurrent load, and you scale further across multiple GPUs and nodes. We size and tune the deployment to your target latency and requests per second.
Ollama is ideal for getting a private LLM running quickly for a team or internal use. vLLM is the better engine when you need high-throughput, concurrent serving for a production application. We build both and will recommend the right fit.
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.