GPU and LLM infrastructure hosted in Türkiye
Everyone has a prototype now. The difference is what keeps it alive in production.
Every one of our B200, H100 and A100 cards sits in Türkiye. You start on a single GPU pod; when the traffic arrives we move you to a dedicated cluster. Your data never crosses the border.
The demo ran perfectly. Then the real traffic showed up.
The GPU queue, the bill nobody can predict and the question of where the data sits — all three tend to land in the same week.
The same three questions come up in every call. Our answers do not change.
Where the data sits, how much you have to commit, who keeps the infrastructure standing.
Your data never crosses the border
Both the model and the data run in data centres inside Türkiye. You meet data residency and Turkish data protection requirements through the infrastructure itself, not through a promise on paper.
From one GPU to your own cluster
Open a pod and try it. If it works, you grow. We do not make you sign an annual commitment to get started.
The infrastructure is our job
Drivers, libraries, scaling, monitoring, the 3 a.m. page. All ours. You look after your model.
Three steps. You do not need anyone else in between.
From opening your first GPU to carrying live traffic.
Pick your capacity
(01)Testing, inference, fine-tuning or development. Start on a single pod and pay only for the hours you use.
Run your workload
(02)Language models, embeddings, agents, batch jobs. Your own application included — it all runs on the same cluster.
Go to production
(03)We move you onto dedicated capacity. As your traffic grows the cluster grows with it — and you never notice it happening.
Stop juggling five providers. It is all here.
GPU cloud, LLM serving, fine-tuning, embeddings and agent workloads — one cluster, one invoice, one support line.
You connect to language models through ready API endpoints. Building, updating and keeping the serving layer alive is our problem.
Our own infrastructure, end to end, with no foreign dependency.
Built for live traffic, not for demos.
From the first model trial to the midnight surge — the compute and model access your team actually needs.
LLM inference
Serve your model without standing up a server or thinking about scaling.
Training and fine-tuning
We reserve the GPUs up front, so your training job never sits in a queue.
RAG and enterprise search
Assistants that answer from your own documents, and cite them.
AI agents
Model access does not drop, so your agent chain never stops halfway.
Image and video generation
Run GPU-hungry generation workloads with as much capacity as your product demands.
Private AI for enterprises
We build you a private system whose data never leaves the building.
When one node both reads and writes, each slows the other down. So we split them.
Our LLM serving cluster is 10 nodes and 80 GPUs. Reading runs on six nodes, answer generation on four. The result: the 300-page file somebody else uploaded does not hold up your answer.
The request arrives
The model reads a 256-thousand-unit (token) context in a single pass. You send a contract of several hundred pages without splitting it into chunks.
256K contextReading nodes
Only these six nodes read the incoming text end to end. Because long documents are routed to this group, you never wait behind the huge file somebody else uploaded.
6 nodes · 48 GPUsWriting nodes
Only these four nodes generate the answer word by word. With the reading load off them, they write at the same speed at 10 a.m. and at 3 p.m.
4 nodes · 32 GPUsThe answer comes back
Data moves between the two groups over a separate link that connects the nodes. You see the first word in under a second, typically.
First word < 1 sShared answer memory
What the cluster works out from every document it reads goes into a single pool all ten nodes share. Ask a second question about the same document and the model does not read it again — it answers straight away.
Under the hood
What the deployment looks like technically, for the engineering teams evaluating it.
The answers your audit will ask for are already prepared.
We cover the items your enterprise audit process will put in front of you at the infrastructure layer.
What changes when you work with baykAI
- Infrastructure with no foreign dependency
- Encrypted, isolated working environment
- A private model trained on your own data
- API-ready model access from day one
- From a single GPU pod to a dedicated cluster
- Deploy in our cloud or your own data centre
- 24/7 cover in production
From the baykAI team
Infrastructure insights, AI updates and practical guides for enterprise teams.
What people ask most before deciding.
The things teams ask most about baykAI's GPU and LLM infrastructure.
Getting started
We give you the GPU capacity to run your models, ready-made language model services, and the support to take them live. All from one place.
Infrastructure and models
Security and compliance
Let's open a pod. Let's see what you are running, together.
Tell us what you want to do; we will work out the right GPU, the right model and the right setup.