Over the past year, local LLMs (large language models) have moved from experimental projects to a real demand from businesses. Large organizations increasingly say the same thing: We need AI - but our data must stay inside the company.
No cloud. No data sharing with third parties. Full control over what the model knows and how it responds.
On paper, everything looks simple: take an open-source model, deploy it on-premise, connect corporate data - done. In practice, almost every local LLM project runs into unexpected challenges.
Below are the key problems we encountered while working with local LLMs - and the approaches that actually worked.
1. The illusion: “A good model will figure everything out”
One of the first mistakes is expecting a modern LLM to automatically “understand the business context” without additional configuration. At the beginning, everything looks promising: the model answers coherently, confidently, and formulates thoughts nicely.
But over time, complaints start to appear:
- answers are too generic
- internal terminology gets mixed up
- the model confidently mentions things that don’t exist in the company at all
The problem is not the model quality. According to the Stanford AI Index, up to 40% of errors in enterprise AI systems are caused not by LLM architecture, but by the lack of domain adaptation.
We came to a simple conclusion: a general-purpose model ≠ a useful business model.
What worked in practice:
- strict limitation of the knowledge domain
- system-level instructions instead of “free conversation”
- reduced creativity in favor of accuracy
- focus on specific business scenarios rather than a “chat about everything”
After this, the model stops being a “smart conversationalist” and starts working as a tool.
2. Infrastructure: when GPUs exist, but performance doesn’t
The second unexpected challenge was performance.
It often feels like a simple fix: “Add more GPUs and everything will fly.”
In practice, without proper architecture, it doesn’t work. We encountered situations where:
- some requests overloaded the system
- others sat idle
- latency increased while total cost of ownership grew alongside it
According to McKinsey and NVIDIA, 30–35% of on-prem AI infrastructure costs are lost due to inefficient architecture.
What helped stabilize the system:
- separating inference and training pipelines
- multi-layer request processing
- caching recurring scenarios
- moving part of the business logic before calling the LLM
At some point, it becomes clear: a local LLM is not “a server with a model” - it’s a full-fledged platform.
3. The data exists - but for AI, it’s as if it doesn’t
One of the most painful stages is working with corporate data.
Formally, everything is there: instructions, policies, reports, PDFs, spreadsheets. But the LLM quickly reveals reality:
- multiple versions of the same document
- conflicting regulations
- outdated information
- lack of structure and metadata
60–70% of AI projects never reach production specifically because of data issues.
A local LLM doesn’t fix this - it amplifies the chaos.
The working approach looked like this:
- inventory of knowledge sources
- defining a “single source of truth”
- normalization and validation of documents
- RAG architecture with intentional search logic, not “search the entire database at once”
After this, answers stop being “almost correct.”
4. Local ≠ automatically secure
Another popular myth is that running a model locally automatically solves security concerns.
In reality, risks don’t disappear - they just change form:
- data access through uncontrolled prompts
- leaks via logs
- overly broad user permissions
Over 45% of enterprise AI incidents are caused by internal access errors, not external attacks.
That’s why security for local LLMs requires:
- role and scenario separation
- prompt and query auditing
- strict context control
- the model’s ability to honestly say: “There is not enough data to answer this”
5. Why employees don’t start using LLMs “on their own”
The final surprise is users.
The expectation that employees will interact with a corporate LLM the same way they use ChatGPT rarely holds true. Without structure, trust disappears quickly.
What works best is not an empty chat interface, but:
- predefined workflows
- prompt templates
- built-in guidance
- training based on real work scenarios
A corporate LLM is also a product - for employees. And it requires a product mindset.
Conclusion
Local LLMs truly give businesses control, security, and independence from external providers.
But what really works is not the model itself - it’s the combination of:
- architecture
- data
- processes
- accountability
Companies that understand this get real results. The rest end up with a polished demo - and disappointment.
Thinking about a local LLM? Start with a clear assessment of data, architecture, and use cases.
Contact us to discuss where it will create real impact.
30/01/2026
Contact us and together we'll figure out how to make your ideas to reality.
Contact us
