Deploy your own private LLM, on infrastructure you control.
LLM Hangar provisions a GPU server in your AWS, Nebius, RunPod or Verda account and gives you a private, key-authenticated OpenAI-compatible endpoint. Your prompts and responses stay on your instance.
No credit card required · one deployment at a time
Pick an LLM and deploy it
to your GPU provider
to your GPU provider
Set a budget cap and
auto-destruct timer
auto-destruct timer
Delete everything, verified
clean, no surprise bills
clean, no surprise bills
Your data, your account,
your region EU
your region EU
No command line or Linux experience needed. No Terraform to write, no AMI to build, no engine to configure.
Private LLM hourly costs
Two calculators on these prices: what a model costs to host per month, and what your own machine can run.
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Own your infrastructure
The instance, the disk, the network and the endpoint belong to your account. You can open them in your own console, put them behind your own policies, and keep them if you stop using us.
No lock-in: resources stay after you cancel
Your provider rates, your reserved capacity, your credits
Access granted by a role you can revoke in one command
WHEN
WHAT
WHERE
08:03:08
Plan recorded: 1 instance, 1 security group
us-east-1
08:04:20
GPU instance created, billing starts
g6e.xlarge
08:19:12
Endpoint live and answering
203.0.113.10
21:44:03
Destroyed and swept, zero resources remaining
us-east-1
Monitor and log end to end
You see what ran, where, when and how. Every action we take in your account is written to a timestamped log, including the plan of what will be created before it exists.
The log exports as JSON, is kept after teardown, and sits alongside spend against cap and request counts read from your own gateway.
Private by construction
Your model, your machine
Weights are downloaded onto a GPU instance inside your own cloud account. No shared tenancy, no queue behind other customers, no third party holding your model.
Prompts never reach us
Requests go straight from your client to your endpoint. We do not proxy, store, log or train on your content, and we could not read it if we wanted to.
EU-only infrastructure
EU
One checkbox pins every resource to EU member-state regions and keeps it there.
eu-central-1
eu-west-1
eu-west-3
eu-north-1
eu-south-1
eu-south-2
Access you control
One endpoint key, shown once and stored only as a hash. Rotate it or delete the deployment and it stops working immediately. On AWS we borrow a role for an hour at a time, scoped to resources we tagged; delete the stack and our access ends.
BUILT TO MEET
GDPR data residency
No sub-processing of prompt data
Encryption in transit
Full audit trail
Verified deletion
Pricing
One flat subscription for the platform. GPU hours are billed to you by your cloud provider.
Launch discount
50% off
Lab
Evals, prototypes and side projects.
$39
/month*
$39
Hardware1 GPU
Deployments at once2
Modelswhole catalog
ProvidersAWS, Nebius, RunPod and Verda
History after teardown30 days
Supportemail
7-day free trial, no credit card required.
* The subscription covers the deployment platform. GPU and hosting charges are billed by your infrastructure provider, like AWS or RunPod, directly to you.
Included on every plan, including the trial
Hard budget caps
Required on every deployment. You can raise the cap; you cannot disable it.
Self-destruct timers
Set a lifetime up front so a forgotten GPU cannot run all weekend.
Verified teardown
A sweep after every destroy, with a record of what was removed.
Full audit trail
Every action we took in your account, timestamped, including the pre-flight plan.
EU-only residencyEU
One checkbox pins a deployment to EU-member-state regions.
Delete
Delete any deployment, or your whole organization, at any time on any plan.
Privacy and safety are not premium features.
Frequently asked questions
How does the 7-day free trial work?
The trial runs for 7 days and no credit card is required to start it. You get the Lab feature set with one deployment at a time. Subscribe whenever you are ready; if the trial ends first, new deployments are blocked until you do. Nothing is destroyed: anything still running keeps running and keeps billing to your cloud account.
What does a stopped deployment cost?
No GPU hours. A stopped deployment keeps its configuration, key and staged weights; only the staged weights bill, at your provider's storage rate, and that line is shown before you confirm. The plan fee is flat and does not change with the hours a deployment runs. See stop and start.
Do you resell GPU capacity?
No. Everything runs in your account at your provider's rates. We never see your infrastructure invoice.
What access do you need to my cloud?
On AWS, a role we can borrow for an hour at a time, scoped to resources we tagged ourselves. Delete the CloudFormation stack and our access is gone instantly.
What if a deploy fails halfway?
We explain the cause in plain language, remove whatever was created, and show the sweep result. The most common first failure is an AWS GPU quota of zero, which comes with a direct link to the form that fixes it.
From the blog
All posts →Nemotron's IOI 2026 gold: 760 GPUs, 1,000 tries
Nvidia's Nemotron beat the top human at IOI 2026. The paper's own numbers show the model answering once scored 304, and what the rest of the score cost in GPUs.
Qwen3.8-27B hardware requirements: what one cloud GPU needs
Qwen3.8-27B is a dense 27B Apache 2.0 model. What it needs in VRAM at bf16, 8-bit and 4-bit, which single cloud GPU fits it, and what that GPU costs per hour.
Muse Glimmer 30B hardware requirements: what one GPU needs
Muse Glimmer 30B is a dense Apache 2.0 model with image input. VRAM at bf16, 8-bit and 4-bit, which single cloud GPU fits it, and the hourly price.
GLM-5.3-Flash (Ox Alpha) hardware requirements and license
Ox Alpha was GLM-5.3-Flash. Z.ai released the weights under MIT on 26 August 2026. The 320B/18B MoE, the 306 GiB FP8 checkpoint, a measured 34-minute cold boot on 4x H200, $/hour.
Is Ox Alpha open weights? Yes: it is GLM-5.3-Flash, MIT
Yes, since 26 August 2026. Z.ai confirmed Ox Alpha was GLM-5.3-Flash and published the weights under MIT. What it is, what it runs on, what the clues got right.
GLM-5.3 hardware requirements and open-weights release date
Weights are expected in late August. What is public so far, when to expect the weights, and the hardware you will likely need.
DeepSeek V4 Flash requirements: 2x H200, boot time, $/hour
Measured cold boot, GPU requirements, and what it costs to run the largest catalog model privately.
Your first private model can be running within the hour.
No credit card required. Connect a cloud account and pick a model.