Dedicated GPU ServerHosting for AI
Run inference, fine-tune models and deploy your own LLM on GPU hardware sized to the workload, supported by engineers in your 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
- GPU Dedicated to You
- Nothing Leaves Your Network
- Engineers in Your Time Zone
- Sized to the Workload
Dedicated GPU Capacity
No time-slicing across tenants, so throughput doesn't dip because someone else's job spiked.
Private Network Isolation
Inference and fine-tuning run on hardware you control, not a third-party API.
Fixed Monthly Pricing
A fixed monthly cost, unlike per-token pricing that climbs as usage grows.
Local Engineering Support
No overnight wait for a ticket to reach someone in a different time zone.
AI Server Hosting Plans
Model size sets the GPU memory you need; concurrent requests set how much of it you can spare. Treat these as starting points — the actual configuration is agreed once we've seen the workload.
| Tier | Typical use | GPU memory | System RAM | Storage |
|---|---|---|---|---|
| Entry | Small models, embeddings, early testing | 16–24 GB | 64 GB | 1 TB NVMe |
| Standard | 7B–13B inference, RAG serving | 24–48 GB | 128 GB | 2 TB NVMe |
| Performance | Bigger models, more concurrent requests | 48–80 GB | 256 GB | 4 TB NVMe |
| Multi-GPU | Fine-tuning, models too large for one card | 2× or more | 512 GB | 8 TB NVMe |
We quote per configuration once the workload is sized — GPU prices shift often enough that a published rate card would be wrong within weeks. Configurations outside this table can be built on request.
Run Your AI Models on Dedicated GPU Hardware
AI server hosting places GPUs — processors built for the parallel computation machine learning models require — inside a server dedicated to one customer, so models run on infrastructure that customer controls rather than a shared third-party API. Two situations justify it: data that legally cannot leave the organisation, and usage heavy enough that a fixed server cost beats per-token billing. Below a certain volume, calling an API is still the cheaper option.
Key Features of AI Server Hosting
VRAM is the real limit. A 70B-parameter model at full precision needs roughly 140GB of GPU memory regardless of how fast the card's cores are — miss that number and nothing else about the hardware matters.
- The whole GPU, all the time
- One card assigned to one customer. Inference latency stays flat because nobody else's batch job is competing for the same cores at 2am.
- Drivers and runtime already working
- CUDA, the inference server and the model runtime installed and tuned before handover, so you get a machine that already runs your model, not a blank Ubuntu box.
- Private by default
- Firewalled networking and no shared storage. Regulated data that cannot leave your premises gets deployed inside your own facility.
- Alerts before capacity runs out
- GPU utilisation, VRAM headroom, temperature and inference latency tracked continuously, with a warning while there is still room to add capacity.
- Support that has deployed models before
- Questions get answered by people who have run inference in production, not a hosting helpdesk working from a script.
Business Benefits of AI Server Hosting
- 0
- Requests and responses stay on hardware you control.
- 1×
- A flat monthly number, not a bill that climbs with every token.
- ↓
- A shorter network hop than a round trip to an overseas API.
- ✓
- Known ceilings you set, not a quota a vendor can change without notice.
Third parties touching your data
One predictable bill
Latency from local hosting
Capacity you can plan
How Our AI Server Hosting Process Works
Nothing gets quoted before it's tested. Guessing at GPU capacity is an expensive way to discover you bought more than the workload needed.
Swipe to see all 5 steps →
Common Use Cases for AI Server Hosting
Private LLM inference
A self-hosted language model answering requests from internal tools.
RAG and knowledge base serving
Generating embeddings and running retrieval over documents that never leave the organisation.
Fine-tuning on your own data
Training an open model on your own records without any of it reaching a third party.
Speech and document processing
Bangla transcription and document OCR processed locally, at volume.
- Financial Services
- Healthcare & Clinics
- Government & NGO
- IT & Software
- Telecom & Utilities
- Education & Training
- Manufacturing
- Media & Publishing
How this might play out
A realistic, hypothetical scenario to show what changes — not a real client or a specific deployment.
A software company prototypes an internal AI assistant against a third-party API. It works, but every request sends customer code and support tickets outside the building, and the invoice grows in lockstep with how much the team actually relies on it.
The same models now run on a GPU server inside their own network. Nothing leaves the building, and the monthly cost is the same whether the team sends a hundred requests a day or ten thousand.
Frequently Asked Questions
01Is this cheaper than using an AI API?
Usually, yes, at sustained high volume — a dedicated GPU costs the same whether it's idle or maxed out, so idle time is wasted spend. At low or occasional usage, an API tends to win. We model both against your expected traffic and tell you which comes out cheaper.
02Which GPU do we need?
Memory decides it first, throughput second. A 7B model running quantised needs a fraction of what a 70B model at full precision needs. We size the card from your actual model, not a generic recommendation.
03Can the server be placed in our own data centre?
Yes. If your organisation can't host outside its own premises, we specify, supply and configure the hardware on site.
04What happens if we outgrow the configuration?
Capacity gets added — a bigger configuration, or more servers behind a load balancer. We map that path out at setup, so growth doesn't turn into a migration project.
05Do you help deploy the model, or only supply the server?
Both. Most clients take the configured route, where we install the runtime and deploy the model ourselves. Some prefer a bare machine and handle the rest with their own team — that works too.
06What uptime can we expect?
A specific figure written into the agreement, with maintenance windows stated up front. We'd rather commit to a number we can hit than quote one that sounds better and isn't true.
07Is our data isolated from other customers?
Yes — the GPU is dedicated, storage isn't shared, and networking is private. On-premise deployment removes the question completely, for anyone who needs that level of certainty.
Related services
- Private & Local AI DeploymentWhat actually runs on top of this hardware.
- Dedicated Server HostingThe same isolation, without a GPU attached.
- AI Knowledge Base & RAGThe workload we see on this hardware most often.
- AI Document IntelligenceBulk document processing that benefits from local GPU.
- Voice AI SolutionsSpeech models that need the same kind of hardware.
- SaaS Product DevelopmentFor when AI is a feature inside a larger product.
Ready to Deploy Your AI Workload?
Talk with our engineers about the model and traffic you're running, and get a GPU configuration sized honestly to what you actually need.