A portfolio instead of one model
A fast smaller model can handle classification and routine work while a stronger model handles complex analysis. A router selects by task.
LOCAL LLM / INFRASTRUCTURE
Local LLM for business: performance, privacy and predictable operations.
A local language model makes sense where sensitive documents, custom integration rules, predictable cost or the ability to operate without a public AI service matter.
01 / PRINCIPLE
The best model is not automatically the best system.
Local LLM quality depends on the combination of model, quantization, context, retrieval, tools, latency and operating limits. Selection therefore has to be based on real tasks.
RAG before uncontrolled training
Current company knowledge is attached at query time with source and access metadata, so content can change without retraining the base model.
Measured capacity and availability
Track concurrent users, context length, cache, latency, VRAM use and behaviour under peak load.
02 / IMPLEMENTATION
Local, hybrid or governed cloud
Local inference fits the most sensitive data and stable workload. A hybrid model can split tasks between owned infrastructure and external models according to data classification. A model gateway unifies APIs, logging, limits and later model replacement.
- 01Benchmark on your data
Test accuracy, citations, language quality, latency and cost on representative scenarios.
- 02Capacity model
Estimate concurrency, standardized A4 workload, context length, cache, availability and user growth.
- 03Secure API
Authentication, tenant separation, limits, logging and tool control.
- 04Update plan
Model versions, regression tests, rollback and change documentation.
03 / DECISIONS
Questions to resolve before deployment.
Scope, architecture and automation level depend on data sensitivity, the workflow and accountability for the outcome.
How large a model does a company need?+
Size depends on tasks, language, target latency and available hardware. Many workflows are better split across several specialised models.
Is a quantized model always much worse?+
No. The impact depends on the quantization method and task. A benchmark on your data matters more than a generic leaderboard.
Can the model later be replaced without rebuilding the application?+
Yes, if the application is separated from inference through a stable model gateway and a regression test set. Portability is an architectural requirement.
Benchmark before you standardise.
We compare candidate models on representative tasks, capacity limits and cost before selecting the production serving path.