LM Studio Hosting on Your Own VPS
Serve open-weight models from your own machine, with an OpenAI-compatible API.
Size a server for LM Studio
Every stop is a real configuration you can deploy. Priced per 30 days, billed by the hour, exclusive of 18% GST.
What LM Studio is
LM Studio runs open-weight language models locally and exposes them through an OpenAI-compatible HTTP endpoint. Anything already written against that API — a client library, an agent framework, an internal tool — can be pointed at your own server by changing one base URL.
The reason to self-host inference is usually confidentiality rather than cost. Prompts and completions never leave the machine, which settles questions that are otherwise difficult when the data is customer records, contracts or source code.
The honest constraint is hardware. These are CPU VPS plans, not GPU instances. A 7B or 8B model quantised to 4-bit will run and answer usefully, but at a pace measured in a handful of tokens per second rather than the instant response a GPU gives. That is fine for batch classification, extraction or summarisation over a queue. It is not fine behind an interactive chat window, and you should size expectations accordingly.
Why run LM Studio on a VPS
Prompts and completions stay on your own machine.
An OpenAI-compatible endpoint, so existing clients need only a new base URL.
No per-token billing — the cost is the server, whatever the volume.
Root access to choose models, quantisation and thread counts.
LM Studio features
OpenAI-compatible chat and completion endpoints
Open-weight models in GGUF, at your choice of quantisation
Configurable context length and thread count
Several models available from one endpoint
Runs headless as a service
Your own domain with TLS in front
Recommended server
A starting point for LM Studio, not a hard floor — size it on the sliders.
| Memory | 16 GB — the model weights sit in RAM, and this is the real constraint |
|---|---|
| CPU | 8 vCPU; CPU inference scales with cores and clock |
| Storage | 80 GB — model files are several GB each |
| Operating system | Ubuntu 22.04 LTS |
How deployment works
LM Studio runs headless with its server mode enabled, behind a reverse proxy with TLS and authentication. Expect CPU-speed inference: a 7B model at 4-bit quantisation is usable for batch work, not for interactive chat. Do not leave the endpoint open — an unauthenticated inference API is someone else's free compute.
What people run it for
Confidential inference
Classification or extraction over data that cannot go to a third-party API.
Batch processing
Queue-driven summarisation where throughput matters and latency does not.
Development against a local endpoint
Build and test without spending on tokens.
Air-gapped-style workflows
Inference reachable only from your own private network.
LM Studio hosting questions
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Root access, your choice of size, and no container between you and the application.