Private reasoning workloads

DeepSeek GPU Server

Private DeepSeek-style model hosting for teams that need controlled inference.

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NVIDIA GPU optionsHigh RAM configurationsNVMe SSD storageLinux deployment
DeepSeek GPU Server preview
What it is

DeepSeek GPU Server hosted by Hosthink

Deploy DeepSeek-compatible open models on dedicated GPU infrastructure for private reasoning, coding, and internal AI workflows.

What it isPrivate DeepSeek-style model hosting for teams that need controlled inference.
Who it is forCode assistants · Private reasoning · Internal knowledge tools · AI workflow backends
Private reasoning workloadsNVIDIA GPU options · High RAM configurations · NVMe SSD storage · Linux deployment · Private access control
Workflow path

DeepSeek GPU 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.

GPU Starter

Indicative starting price
$299/mo
Single GPU
Quantized modelsGPU inventory and final sizing confirmed before deploymentManagement scope confirmed before deployment
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GPU Pro

Indicative starting price
$599/mo
High VRAM GPU
Production inferenceGPU inventory and final sizing confirmed before deploymentManagement scope confirmed before deployment
View GPU Inventory

GPU Advanced

Indicative starting price
$1199/mo
Multi-GPU ready
Large deploymentsGPU inventory and final sizing confirmed before deploymentManagement scope confirmed before deployment
View GPU Inventory
Use cases

Real workflows this supports

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

Code assistants

Private reasoning

Internal knowledge tools

AI workflow backends

Features

Features

01

Private model endpoint

02

GPU acceleration

03

High VRAM options

04

No external AI dependency

05

SSH access

06

Deployment guidance

Technical specs

Production baseline

01

NVIDIA GPU options

02

High RAM configurations

03

NVMe SSD storage

04

Linux deployment

05

Private access control

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

Do you host DeepSeek API keys?
No. This page is for private GPU servers capable of running compatible open model deployments.
Can larger models run?
Yes, subject to GPU memory and quantization strategy. We can size the server before deployment.
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

DeepSeek GPU 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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