GPU Servers for Running Your OwnAI Models
Inference, fine-tuning and private LLM deployment on dedicated GPU hardware — sized to your actual workload, with local support in the same time zone.
Incident — gpu-node-01 · Dhaka
Resolved automatically- ✓
Traffic spike detected — 3.2x normal load
- ✓
Second GPU node brought online
- ✓
Requests rebalanced across both nodes
- ✓
Alert closed — no action needed
0
Requests dropped
40s
Time to scale
41ms
p50 inference
- Dedicated GPU
- Data stays private
- Local support
- Sized to your workload
Dedicated GPU, not shared
The whole card, so performance does not depend on a neighbour.
Your data stays yours
Nothing sent to a third-party model provider.
Predictable monthly cost
A fixed bill instead of per-token charges that scale with success.
Support in your time zone
Engineers reachable during your working day.
Choosing a configuration
The right tier is set by the size of the model you intend to run and how many requests must be served at once. These are typical starting points, not a fixed catalogue.
| Tier | Typical use | GPU memory | System RAM | Storage |
|---|---|---|---|---|
| Entry | Small models, embeddings, prototyping | 16–24 GB | 64 GB | 1 TB NVMe |
| Standard | 7B–13B model inference, RAG serving | 24–48 GB | 128 GB | 2 TB NVMe |
| Performance | Larger models, higher concurrency | 48–80 GB | 256 GB | 4 TB NVMe |
| Multi-GPU | Fine-tuning, very large models | 2× or more | 512 GB | 8 TB NVMe |
Pricing is quoted per configuration after we size your workload — GPU costs move quickly and a fixed public rate card would be out of date within weeks. Configurations outside this table are available.
What is AI or GPU server hosting?
AI server hosting provides servers fitted with GPUs — processors suited to the parallel computation that machine learning models require. It is used to run language models, image models and other AI workloads on infrastructure you control, rather than calling a third-party API. The two reasons organisations choose it are data control, where records cannot be sent outside the organisation, and cost predictability at sustained volume, where a fixed monthly server cost can be lower than per-token API charges. At low or intermittent usage, an API is usually cheaper.
Hardware matched to the model you actually run
GPU memory is the constraint that decides everything. A model that does not fit in VRAM does not run, however fast the card is.
- Dedicated GPU allocation
- The full card assigned to you, not time-sliced across tenants — so inference latency stays consistent instead of varying with whatever someone else is running.
- Configured for your stack
- Drivers, CUDA, inference server and model runtime installed and tuned, so you receive a working environment rather than a bare machine.
- Isolated and access-controlled
- Private networking, firewall rules and no shared storage. For regulated data, deployment inside your own facility instead of ours.
- Monitoring on the things that matter
- GPU utilisation, VRAM headroom, temperature and inference latency — with alerts before capacity becomes an outage.
- Engineers who know the workload
- Support from people who have deployed models rather than a general hosting helpdesk reading a script.
What changes after deployment
- 0
- Models run on hardware you control.
- 1×
- Fixed monthly cost rather than usage-based charges.
- ↓
- Shorter round trip than calling an overseas API.
- ✓
- Known limits instead of a third party's rate caps.
Data sent to third parties
One predictable bill
Latency from local hosting
Capacity you can plan
From a workload to a running server
We benchmark before we quote. Buying GPU capacity on an estimate is an expensive way to find out you needed less.
Swipe to see all 5 steps →
Common workloads
Private LLM inference
Serving a self-hosted language model to internal applications.
RAG and knowledge base serving
Embedding generation and retrieval over a private document set.
Fine-tuning on your own data
Adapting an open model to your domain without sending data out.
Speech and document processing
Bangla transcription and OCR at volume, run locally.
- Financial Services
- Healthcare & Clinics
- Government & NGO
- IT & Software
- Telecom & Utilities
- Education & Training
- Manufacturing
- Media & Publishing
What happened this month
Overview — last 7 days
Live0
Requests dropped
41ms
p50 latency
99.9%
Uptime 30d
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Events
Auto-scale
Handled3.2x load spike · 2nd node added
Model
No downtimeReload completed · 12s to switch
Alert
ClearedTemp 78°C for 4 min
Load handled, latency held and scaling events resolved without anyone paged. Sample data shown.
Questions, answered
01Is this cheaper than using an AI API?
At sustained high volume, usually yes. At low or intermittent usage, no — a dedicated GPU costs the same whether it is busy or idle. We model both against your expected usage and will tell you if an API is the better answer.
02Which GPU do we need?
It is decided by the memory the model requires, then by throughput. A 7B model quantised has very different requirements from a 70B model at full precision. We size it from your actual model rather than recommending a default.
03Can the server be placed in our own data centre?
Yes. For organisations that cannot host outside their premises, we specify, supply and configure the hardware on site instead of in a facility of ours.
04What happens if we outgrow the configuration?
Capacity can be added, either by upgrading the configuration or adding servers behind a load balancer. We plan the path at setup so growth is not a migration.
05Do you help deploy the model, or only supply the server?
Both are available. Most clients take the configured option, where we install the runtime and deploy the model. If your team prefers a bare machine, that is fine too.
06What uptime can we expect?
We commit to a specific availability target in the agreement, with the maintenance windows stated. We would rather write a number we can meet than advertise one we cannot.
07Is our data isolated from other customers?
Yes. The GPU is dedicated, storage is not shared, and networking is private. For the strictest requirements, on-premise deployment removes the question entirely.
Related services
- Private & Local AI DeploymentThe software layer that runs on this hardware.
- Dedicated Server HostingSingle-tenant hardware without GPU.
- AI Knowledge Base & RAGThe most common workload hosted here.
- AI Document IntelligenceDocument processing at volume.
- Voice AI SolutionsSpeech workloads needing GPU capacity.
- SaaS Product DevelopmentIf AI features are part of your product.
Tell us the model and the request volume
That is enough for us to size the hardware — and to tell you if an API would serve you better at your current scale.