Local model serving

Ollama GPU Server

Private Ollama inference on GPU-backed dedicated infrastructure.

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NVIDIA GPU optionsDedicated CPU and RAMNVMe SSD storageUbuntu-ready deployment

What it is

Ollama GPU Server hosted by Hosthink

Run Ollama on a private GPU server for local inference, internal assistants, and model testing without sending prompts to a shared SaaS runtime.

What it isPrivate Ollama inference on GPU-backed dedicated infrastructure.

Who it is forInternal copilots · Private chatbots · RAG prototypes · Offline inference labs

Local model servingNVIDIA GPU options · Dedicated CPU and RAM · NVMe SSD storage · Ubuntu-ready deployment · Private networking available

Workflow path

Ollama GPU Server setup path

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

Review GPU fit

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

Confirm stack needs

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

Prepare handoff

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

Adjust as usage grows

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

Model size → VRAM

How much GPU memory a model needs

VRAM is the memory on the GPU; server RAM belongs to the CPU. Ollama runs a model fully on the GPU only when it fits in VRAM; otherwise part of it is loaded into system RAM and processed by the CPU, which is slower.

Scroll sideways to see all columns.

Model class	Ollama examples (4-bit Q4_K_M download)	VRAM to plan for
7–8B	llama3.1:8b · 4.9 GB qwen3:8b · 5.2 GB	8 GB+
12–14B	gemma3:12b · 8.2 GB qwen3:14b · 9.3 GB	16 GB+
27–32B	gemma3:27b · 17 GB qwen3:32b · 20 GB	24 GB (short context) · 32 GB+
70B	llama3.3:70b · 43 GB llama3.1:70b · 43 GB	48 GB (short context) · 80 GB+
235B MoE	qwen3:235b · 142 GB	160 GB+ (multi-GPU)
405B	llama3.1:405b · 243 GB	256 GB+ (multi-GPU)

Model weightsAt 4 bits, weights take about 0.5 GB per billion parameters (parameters × bits ÷ 8). Q4_K_M downloads are somewhat larger, so use the Ollama download size as the starting point.

Context and parallel requestsThe KV cache grows with context length and parallel requests; Ollama notes that memory scales with OLLAMA_NUM_PARALLEL × OLLAMA_CONTEXT_LENGTH. Its default context depends on VRAM (4k below 24 GiB, 32k at 24–48 GiB, 256k at 48 GiB and above), so set the context length explicitly when sizing.

When a model does not fitOllama spreads a model across all GPUs in the server if it does not fit on one, and can place layers in system RAM; ollama ps shows the CPU/GPU split. These figures are sizing estimates, not measured speed, latency, or throughput.

Hosthink GPU catalog

Listed GPUs and the model sizes they suit

Read from the public Hosthink server catalog when this page is generated; stock changes over time. Starter, Pro, and Advanced prices are indicative starting points and do not reserve a specific GPU: the GPU model, VRAM, location, and final monthly price are confirmed in the quote against current inventory.

Scroll sideways to see all columns.

GPU	VRAM	Plan for (4-bit)	Catalog status	Locations
1× NVIDIA RTX A4000	16 GB	12–14B	Currently unavailable	Amsterdam
1× NVIDIA GeForce RTX 4090	24 GB	27–32B (short context)	In stock	Moscow
1× NVIDIA RTX A5000	24 GB	27–32B (short context)	In stock	Amsterdam
1× NVIDIA GeForce RTX 3090	24 GB	27–32B (short context)	Currently unavailable	Paris, Amsterdam, Moscow
1× AMD Radeon AI PRO R9700	32 GB	27–32B	Currently unavailable	Amsterdam
1× NVIDIA GeForce RTX 5090	32 GB	27–32B	Currently unavailable	Moscow
2× NVIDIA GeForce RTX 4090	2 × 24 GB = 48 GB	70B (short context)	In stock	Moscow
2× AMD Radeon AI PRO R9700	2 × 32 GB = 64 GB	70B (short context)	Currently unavailable	Moscow
2× NVIDIA GeForce RTX 5090	2 × 32 GB = 64 GB	70B (short context)	Currently unavailable	Paris, Amsterdam
1× NVIDIA H100 80GB	80 GB	70B	In stock	Dallas
4× NVIDIA GeForce RTX 4090	4 × 24 GB = 96 GB	70B	In stock	Keflavik
3× AMD Radeon AI PRO R9700	3 × 32 GB = 96 GB	70B	Currently unavailable	Moscow
8× NVIDIA RTX PRO 6000 Blackwell Server Edition	8 × 96 GB = 768 GB	405B (multi-GPU)	Currently unavailable	Tokyo, Amsterdam, Ashburn +8

11 other GPU listings are not shown because their catalog label does not state GPU memory unambiguously or the GPU is optional; VRAM for those is confirmed in the quote.

HardwareA dedicated server: the GPUs, CPU, RAM, and NVMe storage in your quote are not shared with other customers.

AccessSSH access to the server; you choose which models to pull, run, and expose.

Management scopeWho installs and updates Ollama, GPU drivers, and models, and who monitors the service, is confirmed before deployment.

View GPU servers, prices, and stock · Sources, checked 2026-10-30: Ollama context length, Ollama FAQ, Ollama hardware support, Ollama library.

Pricing

Starter, Pro, and Advanced plans

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

GPU Starter

Indicative starting price

$199/mo

Entry GPU

Small modelsGPU inventory and final sizing confirmed before deploymentManagement scope confirmed before deployment

View GPU Inventory

GPU Pro

Indicative starting price

$399/mo

Mid GPU

Team inferenceGPU inventory and final sizing confirmed before deploymentManagement scope confirmed before deployment

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GPU Advanced

Indicative starting price

$799/mo

High VRAM GPU

Larger modelsGPU 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 Ollama GPU Server inside AI, automation, internal tools, and operations stacks.

Internal copilots

Private chatbots

RAG prototypes

Offline inference labs

Features

Private inference endpoint

GPU acceleration

Model library workflow

SSH access

Dedicated hardware options

No shared tenant runtime

Technical specs

Production baseline

NVIDIA GPU options

Dedicated CPU and RAM

NVMe SSD storage

Ubuntu-ready deployment

Private networking available

GPU inventory and final sizing confirmed before deployment

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.

Lower response latency

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

Larger model headroom

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

Higher concurrency

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

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

Is model data private?

Yes. The server is dedicated to your service and model traffic stays under your deployment.

Can I choose GPU size?

Yes. GPU selection depends on current inventory and model requirements.

How much VRAM does an Ollama model need?

Start from the 4-bit download size in the Ollama library and add headroom for context and runtime: about 5 GB for 8B models, 9 GB for 14B, 20 GB for 32B, and 43 GB for 70B (Q4_K_M). Longer context and parallel requests need more VRAM on top of that.

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

Ollama GPU Server Deploy with Hosthink

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

View GPU Inventory