Self-hosted AI

Private LLM Server

Self-hosted LLM infrastructure for privacy-first teams and regulated workloads.

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Dedicated server optionsGPU acceleration availableNVMe SSD storagePrivate access design
Private LLM Server preview
What it is

Private LLM Server hosted by Hosthink

Private LLM Servers are dedicated environments for organizations that need local inference, controlled data flow, and no dependency on public AI endpoints.

What it isSelf-hosted LLM infrastructure for privacy-first teams and regulated workloads.
Who it is forRegulated AI workflows · Internal knowledge assistants · Private document chat · Offline AI capability
Self-hosted AIDedicated server options · GPU acceleration available · NVMe SSD storage · Private access design · Linux deployment
Workflow path

Private LLM Server setup path

A quick scan of the product flow before you move from page evaluation to a working Hosthink service.

01

Review GPU fit

Start from the GPU server family that matches the model, memory, and workload profile.

02

Confirm stack needs

Choose whether you need Ollama, DeepSeek, a private LLM stack, or a broader GPU AI server setup.

03

Prepare handoff

Hosthink keeps the setup path clear so the server can be connected to your AI workflow.

04

Adjust as usage grows

Move to a larger GPU or stack configuration when model size, concurrency, or storage needs change.

Pricing

Starter, Pro, and Advanced plans

GPU inventory and final sizing confirmed before deployment. Management scope confirmed before deployment.

Private Starter

Indicative starting price
$249/mo
Single GPU ready
Small teamsGPU inventory and final sizing confirmed before deploymentManagement scope confirmed before deployment
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Private Pro

Indicative starting price
$599/mo
High VRAM option
Production useGPU inventory and final sizing confirmed before deploymentManagement scope confirmed before deployment
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Private Advanced

Indicative starting price
$1299/mo
Multi-GPU ready
Enterprise AIGPU inventory and final sizing confirmed before deploymentManagement scope confirmed before deployment
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Use cases

Real workflows this supports

These are practical deployment patterns for teams using Private LLM Server inside AI, automation, internal tools, and operations stacks.

Regulated AI workflows

Internal knowledge assistants

Private document chat

Offline AI capability

Features

Features

01

Private LLM runtime

02

Dedicated hardware

03

Offline-capable design

04

GPU acceleration options

05

Secure network options

06

Managed deployment support

Technical specs

Production baseline

01

Dedicated server options

02

GPU acceleration available

03

NVMe SSD storage

04

Private access design

05

Linux deployment

06

GPU inventory and final sizing confirmed before deployment

07

Management scope confirmed before deployment

GPU vs CPU

GPUs change the shape of AI workloads

CPU-only inference can work for tiny models and background tasks, but interactive assistants, retrieval workflows, and larger local models need parallel acceleration to feel usable.

01

Lower response latency

GPU acceleration helps reduce wait time for chat, code, and agent loops where every generation step matters.

02

Larger model headroom

VRAM determines how comfortably quantized and full-size models can run with useful context windows.

03

Higher concurrency

Teams serving multiple users need predictable throughput, not a single workstation-style process.

04

Private deployment control

You choose the model, runtime, network exposure, and update rhythm instead of depending on an external AI platform.

Recommended workloads

Size the server around the model, not the headline

Small local models

7B-13B
Entry GPU / quantized
Internal assistant prototypesPrompt testing and light RAGSingle-team usage patterns

Production inference

30B-70B
High VRAM recommended
Knowledge assistantsAgent backends and API servingMore concurrency and context

Advanced AI stacks

Multi-GPU
Sized with engineering
Large private LLM deploymentsMultiple model endpointsEnterprise isolation requirements
Recommended stacks

Pair GPU infrastructure with hosted AI tools

Private AI servers handle inference. Hosted apps can provide the user interface, workflow builder, or internal data layer around it.

1-click OpenClaw

A self-hosted AI assistant connected to your messaging channels and tools.

View

1-click Uptime Kuma

Uptime checks, alerts, and status pages.

View

1-click n8n

Workflow automation, webhooks, and API integrations.

View
FAQ

Common questions

Can this run without public AI APIs?
Yes. The point is to keep inference local when the selected model and hardware support it.
Do you help choose the model?
Yes. We can size hardware around the model family, VRAM needs, and expected concurrency.
Do I need a GPU for every AI workload?
Not always. Hosted app workloads can use external model APIs, while private local inference usually benefits from GPU.
Can you size the server?
Yes. We can size around model family, VRAM, concurrency, and storage requirements.
Is this the same as public cloud GPU?
No. These pages position dedicated or private GPU server deployments, not a shared SaaS model endpoint.
Is server RAM the same as GPU VRAM?
No. VRAM is the memory on the GPU, and server RAM belongs to the CPU. If a model does not fit in VRAM, runtimes such as Ollama can load part of it into system RAM, which is slower, so size the GPU around the model.
Does the indicative price reserve a specific GPU?
No. Starter, Pro, and Advanced prices are indicative starting points. The GPU model, VRAM, location, and final monthly price are confirmed in the quote against current inventory.
Private AI Servers

Private LLM Server Deploy with Hosthink

Keep the same Hosthink design, billing, and support flow while adding AI and app workloads to your infrastructure stack.

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