Model selection & sizing
We pick the right open-weight model for your task, language needs and budget. Concurrent users, context length and latency target turn into a concrete GPU and memory requirement.
AI & LLM
Local & Private LLM Deployment
If your data cannot leave the organisation, we bring the AI inside it. We deploy, scale and operate open-weight models on your own servers or private cloud — including fully air-gapped environments.
What you get out of it
- Sensitive data never reaches a third-party server
- Predictable fixed infrastructure cost instead of variable per-token billing
- No vendor lock-in — the model and the infrastructure are under your control
- A documented data flow you can defend under KVKK and GDPR
- A system unaffected by third-party outages or silent model version changes
vLLM
Ollama
llama.cpp
TGI
Docker
Kubernetes
NVIDIA CUDA
GGUF / AWQ / GPTQ
Open WebUI
LiteLLM
WHAT'S INCLUDED
What this service covers
High-throughput serving layer
On vLLM, TGI or llama.cpp we use continuous batching, KV-cache management and tensor parallelism to get several times more throughput out of the same hardware.
Quantisation & cost optimisation
With GGUF, AWQ and GPTQ we shrink models while measuring the quality cost. Fitting a 70B model onto a single box — or running usefully on CPU — is often achievable.
Offline & air-gapped installs
For networks with no internet access we package every dependency, the model weights and the update procedure. Closed-loop installs of the kind public sector, defence and healthcare need are entirely possible.
OpenAI-compatible API layer
We put the local model behind a standard API so your existing applications can switch from a cloud provider with a one-line URL change. Hybrid routing is available if you want it.
Monitoring, quotas & audit
Dashboards for token usage, latency, GPU saturation and error rates; per-team quotas and full request logging. Who asked what, and what the model answered, stays auditable.
PROCESS
How we run it
01
Requirements & hardware analysis
We map use cases, concurrency and latency targets, then tell you plainly whether your current hardware is enough or exactly what to buy.
02
Pilot install
We test on real work with a limited group of users. Model comparison happens here, on your own data.
03
Production install
We build the permanent installation with high availability, authentication, quotas, logging and backups.
04
Training & operational handover
We teach your team to run it: updating models, adding capacity, troubleshooting. Or we keep operating it for you.
COMMON QUESTIONS
Local & Private LLM Deployment
For the hardest open-ended tasks the largest closed models still lead. But for narrow, well-defined enterprise tasks — summarising documents, classification, question answering, filling forms — a well-chosen and fine-tuned open model is usually sufficient and often more consistent. We settle the question with a comparison on your own data, not with guesswork.
For small teams a single professional GPU (24–48 GB) comfortably runs quantised 7B–14B class models. The 70B class needs multiple GPUs or aggressive quantisation. In some scenarios a strong CPU server is enough — we answer this by measuring, not estimating.
Usually not. Because we expose an OpenAI-compatible endpoint, application-side changes are minimal. We migrate gradually: route low-risk traffic to the local model first, measure quality, then widen.
RELATED SERVICES
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Fine-tuning, LoRA/QLoRA, instruction tuning, DPO and evaluation
RAG & Enterprise Knowledge Systems
Answers grounded in your own documents, with citations
AI Adoption Consulting
Where to start, what will pay off, and what will not
Let's talk about where AI fits in your business.
In a short call we listen, then tell you plainly which approaches fit your situation and which do not. We reply within 24 hours.