Local AI inference means running AI models — LLMs, vision models, speech models — on hardware your company owns or manages, without sending data to external cloud providers. The GPU is the critical component: a language model that takes minutes to respond on a CPU responds in seconds on the right GPU.
For companies with confidential data, regulatory constraints, or simply enough usage to make the cloud expensive, building local inference capacity is a strategic decision with a clear return and full sovereignty over your AI.
Why local inference is gaining ground
Privacy and compliance without compromise. In healthcare, legal, banking, or defense, certain data can’t leave the premises. Local inference makes that a technical reality, not just a policy.
Predictable latency. A GPU server on your local network responds in milliseconds, with no dependence on internet latency or your provider’s demand peaks.
Controlled cost at scale. Above certain usage volumes (typically more than 50,000 requests a month), your own GPU server is cheaper than the big providers’ APIs. The hardware payback period depends on inference volume, the cost of the cloud API alternative, and your team’s usage profile.
No vendor lock-in. Open-source models (Llama, Mistral, Qwen, Phi, Gemma) can be downloaded and run without a license. Your company’s code doesn’t depend on a provider changing prices or shutting down the service.
Typical local inference architecture
A well-designed enterprise inference server includes:
- GPU hardware: NVIDIA A100/H100 for demanding production, RTX 4090/A6000 for midsize workloads, Apple M-series for low-power setups
- Serving framework: vLLM (high performance, efficient batching), Ollama (easy management), Hugging Face TGI (flexibility), LM Studio (visual interface)
- OpenAI-compatible API: lets you reuse existing integrations by pointing them at the local server
- Monitoring: VRAM usage, token latency, throughput, and request queue metrics
Choosing a model for each use case
There’s no universal “best model.” The right choice depends on the use case, the language, data sensitivity, and the hardware available:
| Size | Representative models | Hardware required | Best for |
|---|---|---|---|
| 3–8B | Phi, Llama, Gemma | 8 GB VRAM GPU | Simple tasks, classification, extraction |
| 14–32B | Qwen, Mistral, Llama, DeepSeek | 16–24 GB VRAM GPU | Writing, analysis, high-quality RAG |
| 70B+ | Llama, Qwen, DeepSeek | 40–80 GB VRAM GPU | Complex reasoning, code, advanced analysis |
Quantized models (GGUF, GPTQ, AWQ) let you run large models with less VRAM in exchange for a slight drop in quality.
Integration with business systems
Local inference doesn’t work in isolation. To create value, it connects to:
- RAG systems: the GPU server handles semantic search queries over internal documentation
- AI agents: agents call the local LLM for reasoning and to generate actions
- Automation pipelines: n8n, Airflow, or Python scripts that call the model to process documents or generate content
- Internal applications: internal chat, support assistants, report generators
An honest assessment: when is the cloud the better option?
As an independent firm, we have no interest in selling hardware. Local inference makes sense when at least one of three factors is in play: significant usage volume, sensitive data, or demanding latency requirements. When none of them is critical, cloud APIs (OpenAI, Anthropic, Google) are simpler and more flexible to start with.
A production-grade GPU server costs between €10,000 and €30,000. Before making that investment, we work with you to assess your actual usage volume, your privacy requirements, and whether API costs justify the move to your own infrastructure.
Frequently asked questions
What GPU do I need to get started? For experimentation and non-critical use, an NVIDIA RTX 3090 or 4090 (24 GB VRAM, roughly €1,500–2,000) is enough for models up to 30B parameters. For production with latency SLAs, we recommend an NVIDIA A10G, L40S, or A100, depending on volume and requirements.
Is it compatible with the models I already use in the cloud? The leading open-source models (Llama, Qwen, DeepSeek, or Mistral) come close to proprietary models on many tasks. Migrating prompts from the cloud to a local server usually requires only minor adjustments.
Can local inference be combined with the cloud? Yes. The most common hybrid architecture keeps sensitive data local and uses the cloud for low-sensitivity tasks or load peaks. Smart routing can pick the endpoint based on data classification or request type.