AI Solutions / Corporate LLM
Corporate LLM. Run it on your terms.
A corporate LLM gives your business a language model on infrastructure you choose. Hashfox helps you assess open-weight models, provision the right environment and keep the service running, on your own hardware or in a private cloud.
The hardware decision comes after the workload.
Your team sees useful applications for AI. Now someone has to decide where it should run, how much capacity it needs and who owns the operational work. We start with the jobs the model has to perform and how often people will use it, because those answers are more useful than choosing a server first. Whether the result is equipment in your own building, a private cloud or a combination of both is the outcome of that calculation, not its starting point.
Choose a corporate LLM by the work it can do.
A fluent answer is not enough if the model misses a product code or mishandles a document. We compare suitable candidates using representative tasks from your business. Capacity planning then follows the real demands of the model, request lengths, concurrent users and acceptable waiting times. Open weight simply means the trained weights are available; the accompanying licence defines how they may be used, modified and deployed, and that is neither automatically open source nor unrestricted commercial use.
- Review open-weight candidates for task performance, language support and licence terms.
- Evaluate responses against examples your subject specialists can judge.
- Assess existing equipment and work out the memory, processing and network requirements with you, instead of promising a specification up front.
Compare the whole service, wherever it runs.
Owning hardware can make sense when demand is steady enough to use it well. The calculation includes power, cooling, maintenance, replacement and resilience. Private cloud capacity often suits an evolving workload better, and it is an equally valid route rather than a compromise. What matters is the total effort per useful result, and we compare that for both routes as well as for a managed model service.
- Configure access, network boundaries and connections to the applications you already run.
- Monitor capacity, response times and service failures; check model and software updates before release, with a route back if needed.
- Document configuration, recovery procedures and responsibilities. We support customer systems in Germany and the EU, with the location and service scope agreed around your requirements.
When we are not the right fit
We will not sell you an AI server simply because self-hosted models are a popular topic. If running one adds work without enough value, we will recommend another route. And if finding internal documents is the main objective, we start there. Running your own model is a separate choice.
- Usage is occasional: dedicated capacity may spend most of its time idle, and a suitable managed service can be the more sensible option.
- One model must excel at everything: the selected model has to meet your acceptance criteria, and more equipment does not automatically close a capability gap.
- Nobody will own the service: access, updates and incidents need named responsibilities, whether the work stays internal or is assigned to us.
Make the decision testable before making it permanent.
- 1
Define the workload
We establish what the model must do, where data comes from and how the service will be used. Existing infrastructure and operating constraints are part of the brief.
- 2
Prove the fit
Candidate models run representative tasks. We compare acceptable output, performance and total operating effort across the relevant deployment options.
- 3
Build the service
We configure the selected environment and connect your applications. Access controls, monitoring, recovery and acceptance criteria are agreed before production use.
- 4
Operate with a reason to change
We carry out the agreed service responsibilities and assess updates before release. Capacity and model choices evolve when the workload justifies it.
Questions before you run a model yourself
What do you mean by a corporate LLM?
A language model provided for your organisation within an agreed operating environment. It may run on company-owned hardware or in a private cloud. The usual starting point is an existing model selected for your tasks, rather than training a foundation model from scratch.
Is there a minimum company size that makes self-hosting worthwhile?
Headcount alone is a poor guide. Frequency of use, task complexity, peak demand and operational effort matter more. We assess whether the expected workload makes good use of the infrastructure and compare the result with a private cloud and with a managed service.
Do we need a particular type of server?
There is no useful universal specification. Model size, context length and concurrent requests affect memory and processing requirements. We test the intended workload and assess your existing equipment before sizing the environment.
Can the model work offline?
A suitable deployment can process requests locally without an internet connection. The supporting services must be available locally too. Obtaining models, applying updates, remote support and telemetry require separate decisions about permitted connections.
Will it immediately know our business information?
No. Hosting the model and connecting company knowledge are different pieces of work. Where the application needs internal documents, we can add RAG retrieval with appropriate permissions and test the quality of its answers.
Start with the work. We will help you choose where it runs.
Bring your intended use cases, expected demand and a rough view of your current infrastructure. A free initial consultation will establish which deployment options deserve a closer look and what needs testing first.
- Free and without obligation
- No hardware decision required
- An honest assessment, not a sales pitch