OffshoreVps
RTX 4090 · 5090 · H100 Open-weight models No KYC

Private LLM hosting: run open models on a GPU server you control

When you run an open-weight model yourself, your prompts and documents go to a server you rent, not to a third-party API. The question that decides everything is whether the model fits in GPU memory. This page gives you the arithmetic and the matching server, with real prices.

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No KYC Pay in crypto Full root GPU servers 10 locations

Page by the OffshoreVps teamUpdated Prices read from the live catalogue

Will it fit

Which model fits which GPU server

A rule of thumb: weights = parameters × bytes per weight, plus about 20% for working memory. Real needs vary with context length, batch size and software.

Model and precisionEstimated memory1x RTX 4090 (24 GB)1x RTX 5090 (32 GB)1x H100 (80 GB)2x H100 (160 GB)
8B parameters, 16-bit~20 GBfitsfitsfitsfits
8B parameters, 8-bit~10 GBfitsfitsfitsfits
8B parameters, 4-bit~5 GBfitsfitsfitsfits
32B parameters, 16-bit~77 GBnonofitsfits
32B parameters, 8-bit~39 GBnonofitsfits
32B parameters, 4-bit~20 GBfitsfitsfitsfits
70B parameters, 16-bit~168 GBnononono
70B parameters, 8-bit~84 GBnononofits
70B parameters, 4-bit~42 GBnonofitsfits
120B parameters, 16-bit~288 GBnononono
120B parameters, 8-bit~144 GBnononofits
120B parameters, 4-bit~72 GBnonofitsfits

These are estimates for planning, not benchmarks. A model that just fits leaves little room for long prompts, so leave margin. Quantised models lose some quality; test yours.

The servers

GPU servers: pick one and a location

Prices change with the location you choose. A longer term takes up to 50% off.

Location
GPU

Small: 1x RTX 4090, 24 GB

$109.25/mo

or $54.63/mo on a 12-month term (−50%)

  • GPU1x RTX 4090 · 24 GB
  • CPU12 vCPU
  • RAM64 GB DDR5
  • Disk1 TB NVMe

Fits small and mid-size models, and 32B at 4-bit.

Configure and order
Location
Popular
GPU

Medium: 1x RTX 5090, 32 GB

$175.75/mo

or $87.88/mo on a 12-month term (−50%)

  • GPU1x RTX 5090 · 32 GB
  • CPU16 vCPU
  • RAM96 GB DDR5
  • Disk1.5 TB NVMe

More headroom for context and larger quantised models.

Configure and order
Location
GPU

Large: 1x H100, 80 GB

$750.50/mo

or $375.25/mo on a 12-month term (−50%)

  • GPU1x H100 SXM5 · 80 GB
  • CPU24 vCPU
  • RAM192 GB DDR5
  • Disk2 TB NVMe

A 70B model at 4-bit, or 8-bit for mid-size.

Configure and order
Location
GPU

X-Large: 2x H100, 160 GB

$1,415.50/mo

or $707.75/mo on a 12-month term (−50%)

  • GPU2x H100 SXM5 · 160 GB
  • CPU32 vCPU
  • RAM384 GB DDR5
  • Disk4 TB NVMe

Very large models and 16-bit mid-size models.

Configure and order
How to run it

Software for serving a model

  • Ollama or llama.cpp are simple ways to run quantised models and expose a local API. A good start for one user.
  • vLLM is built for serving many requests at once on a GPU. Use it when several people or apps share the server.
  • A web interface such as Open WebUI can sit in front of either, behind a reverse proxy with HTTPS and a login.

Whatever you choose, do not expose the model API to the internet without authentication. Our walkthrough is in run LLMs on a GPU server.

What “private” does and does not mean

Your prompts stay on a server you rent and nobody else shares the GPU. They are not sent to a model vendor. The server is still hosted by us, we do not monitor or log inside your servers, and the rules in our terms apply to everything you run, including what you generate and publish. You are responsible for following the licence of the model you download.

Self-host or use an API

When a private GPU server makes sense

A good fit when

  • You process documents you do not want to send to a vendor
  • You use the model heavily, so a flat monthly price beats per-token billing
  • You want to choose the model and the settings
  • You want to pay without a card or an ID

An API may be better when

  • You use a model rarely: an idle GPU still costs a month
  • You need the largest proprietary models, which cannot be self-hosted
  • You do not want to maintain a server
  • Your model does not fit in the memory you can afford
FAQ

Private LLM hosting questions

01Can I run my own LLM on a rented GPU server?

Yes, if you follow the licence of the model. Choose a server whose GPU memory fits the model with some margin, install a serving tool such as Ollama or vLLM and secure the API.

02How much VRAM do I need for a 70B model?

Roughly 35 GB of weights at 4-bit, 70 GB at 8-bit and 140 GB at 16-bit, plus about 20% for working memory. By that estimate a 4-bit 70B model needs a 80 GB H100, not a 24 or 32 GB card.

03Which GPU server is best for a 7B or 8B model?

The RTX 4090 server fits them at any precision in the table, and the cheapest is $109.25 a month. Larger models need more memory.

04Does private LLM hosting mean uncensored?

No. It means you choose and run the model on your own server. What you generate and publish is still covered by the law and by our terms.

05Do I need KYC?

No. Generate a login and pay in crypto. See no-KYC GPU servers.

06How are GPU servers billed?

Monthly from your prepaid balance, with up to 50% off on longer terms. See pricing.

Your model, your server

Pick a GPU, pay in crypto, log in as root. No ID, no card.