● self-hosted LLMs · your hardware, your data
Run your own LLMs. Nothing leaves your network.
We deploy open-weight models on your own GPUs — on-prem or in your cloud account — behind an OpenAI-compatible endpoint your existing code already speaks. No per-token bill, no data egress, no vendor who can deprecate your model or change the price next quarter.
Most “AI strategy” ends with your proprietary data in someone else’s log file, on a price you don’t control. Self-hosting is not a downgrade — it is ownership.
— Sumit Ghosh, founder
// not a brochure
Our own engineering team runs on this stack, every day.
This is not a service we assembled for a slide. Globussoft’s developers write code against a self-hosted model on our own workstation — the same architecture we deploy for you.
Qwen3-Coder-30B, FP8, 256K context, served with vLLM
NVIDIA RTX PRO 6000 Blackwell (96 GB) + RTX A5000 (24 GB)
Per-developer API keys through our own gateway, every call logged per key
OpenAI-compatible /v1/* and Ollama-native /api/* — existing tools connect unchanged
We built the gateway because we needed per-developer attribution and usage logs for our own team. You get the same thing: not a black box, but an inference layer you can actually audit.
// why self-host
Four reasons teams move off the API.
Data never leaves
Inference happens inside your network. Customer records, contracts, source code and patient data stay on infrastructure you control — which is usually the difference between a pilot and a signed-off deployment.
Cost stops scaling with usage
Per-token pricing punishes the moment a system works and volume grows. Owned hardware turns a variable bill into a fixed one, and the marginal request costs electricity.
The model stops moving
A hosted model can be retuned, rate-limited or deprecated underneath you. A local checkpoint is frozen until you decide otherwise — so your evals stay valid and your outputs stay reproducible.
Latency you control
No public-internet round trip and no noisy-neighbour queue. For high-volume or interactive workloads, the network hop is often the largest single component of response time.
// how we deploy
From “which model?” to production, with one team.
Same embedded model as the rest of our work: a senior engineer stays with it from sizing to handover. No procurement theatre, no offshore relay.
Week 1
Size the problem
Your actual workloads, volumes and latency needs — measured, not estimated. That decides the model class and the GPU budget, and it is where we tell you honestly if a hosted API is still the right answer.
Week 2
Benchmark on your data
Candidate open-weight models evaluated against your tasks, with an eval set we build from your real examples. You see the quality/cost trade-off in numbers before anything is bought.
Week 3–4
Deploy
vLLM on your hardware or your cloud account, behind an OpenAI-compatible gateway with per-team keys, usage logging and quotas. Existing integrations repoint with a base-URL change.
Ongoing
Operate or hand over
We run it, or we train your team and leave — with runbooks, monitoring and the eval harness. Full weights, full config, no license that expires.
// deployment tiers
Three ways to start.
Every tier ships the weights, the config and the eval harness to you. Nothing here is rented.
// what you get
Everything, in your hands.
Open-weight families — Qwen, Llama, Mistral, DeepSeek, Gemma — chosen against your tasks, not our preference
vLLM, with quantisation chosen to fit your hardware and quality bar
OpenAI-compatible endpoint, per-team API keys, per-key usage logs, quotas
Your servers, your cloud account, or an air-gapped network
A harness built from your real examples, so a model upgrade is a measurement rather than a leap of faith
Weights, configs, runbooks and monitoring. No license key, no phone-home, no expiry
// questions
Straight answers.
Will an open model be good enough for our work?
Sometimes yes, sometimes no — and we would rather find out in week two than after you have bought hardware. The benchmark phase runs candidate models against your own tasks and reports the trade-off honestly. If a hosted frontier model is genuinely the right answer for your workload, we will tell you that.
What hardware do we need?
It depends entirely on model size, context length and how many concurrent requests you serve — which is exactly what the sizing phase measures. A single workstation GPU covers a lot of real workloads; heavier ones want multiple data-centre cards. We will not quote a GPU count before measuring yours.
Does our existing code have to change?
Usually just the base URL and the API key. The gateway speaks the OpenAI API, so most SDKs, agent frameworks and editor integrations connect unchanged.
Can this run fully offline?
Yes. Once the weights are on the machine, inference needs no outbound network at all. Air-gapped deployment is supported — tell us at scoping, because it changes how we handle updates and monitoring.
What happens if we want to leave?
You keep everything: weights, configuration, runbooks, eval harness. There is no license server to call home and nothing that stops working when an invoice does. That is the entire point of owning it.
Do you fine-tune?
When the evals show it earns its keep. Prompting, retrieval and model choice solve most problems more cheaply and are far easier to maintain — we reach for fine-tuning when the measurement says to, not by default.
