The era of AI being seen solely as a cloud chatbot is officially over. The rise of Agentic AI and complex multimodal systems has presented companies with a harsh reality. Third-party API bills are becoming unsustainable, and corporate data security has become a critical issue.
The main IT trend is the massive transition to local AI infrastructure (on-premise) and the construction of next-generation data centers. However, deploying a modern AI server is much more complicated than just buying powerful graphics cards.
🛑 The “tokenomics” crisis: Why companies are switching to local AI servers
Not long ago, companies were budgeting millions to use cloud models. However, the constant queries from hundreds of employees and the work of AI agents running in the background led to a phenomenon called “Token Bill Shock.”
In response, a strong trend towards Sovereign AI and local servers has emerged . Instead of sending sensitive information to foreign cloud servers, companies are deploying infrastructure within their own perimeter. The advent of optimized small language models (SLMs) such as Llama 4 Scout, Mistral Small 3 , or Phi-4 has shown that local AI can solve 90% of business tasks without the need for huge subscription investments.
Engineers' opinion on community forums: "Running a local version of Llama 4 on your own server network will pay for itself in a few months if you have a constant flow of internal documents and code analysis. But the key is to calculate the power consumption correctly."
💧 Death of air cooling: Heatstroke in server rooms
A key technical problem with current AI servers is their extreme heat generation. Traditional air cooling (powerful fans and air conditioning) simply cannot handle the density of these calculations.
The latest generation of chips, including NVIDIA Blackwell (GB200) graphics processors and the latest AMD MI series accelerators, require enormous power. The heat load per server rack today exceeds 100-130 kW. To put that into perspective, this heat would comfortably heat an entire apartment building in winter.
The evolution of AI infrastructure cooling:
- Direct-to-chip (Direct liquid cooling): The most sought-after standard today. Special copper plates (cold plates) are mounted directly on the AI chip and processor, with circulating liquid removing heat directly from the source.
- Immersion (direct immersion) cooling: The servers are completely submerged in baths of dielectric fluid. This allows for maximum hardware density in space, although maintenance is a bit more complicated.
By switching to liquid circuits, data centers can eliminate the need for massive external chillers and reduce the power utilization efficiency (PUE) coefficient to record values of around 1.15–1.2.
🛠️ From what build an AI server: Current hardware
For large IT ecosystems, off-the-shelf architectures from Dell, HPE, or custom racks based on NVIDIA Blackwell NVL72 are becoming the standard. However, for mid-sized businesses and local deployments, hybrid and custom configurations are actively being discussed on discussion forums.
The table below compares configurations for running local AI models:
System class Target models Recommended hardware Optimal cooling Local stack (SMB) Smaller models (Gemma 3, Phi-4, Mistral 24B) Workstation with 1-2 RTX 4090 cards (24GB VRAM) or Mac Studio 64GB Advanced Air/Water Closed Circuits (AIO) Corporate AI server Large open-weight models (Llama 4 109B) Server platforms based on NVIDIA H100/H200 or AMD MI300 Direct-to-chip liquid cooling Data center infrastructure Model training, Agentic AI clusters Server cabinets based on NVIDIA Blackwell GB200 architecture Complete liquid cooling (CDU units and cooling towers)
For effective local performance management, engineers use modern software tools – such as vLLM (for high-load enterprise systems with PagedAttention support) or Ollama / LM Studio for rapid deployment of pilot projects.
🔮 The future of AI infrastructure
The AI server industry is rapidly moving away from the concept of “just renting the cloud.” Intelligence per Watt , privacy, and integration flexibility via open protocols are becoming key factors in successful IT architecture .
The winners are those companies that can balance the extreme computing power of next-generation chips with realistic power and cooling options. Local AI has ceased to be an expensive toy for technology enthusiasts - it has become the basis of independence and commercial efficiency of modern business.
