Running AI on your own servers: when it pays off

Running AI locally means a language model runs on your own server, or in an environment you control, and company data never goes to an outside provider. It makes sense mainly when data rules require it, when you have a steady high volume of requests, or when you already operate powerful servers with graphics cards (GPUs). In most other cases the cloud is simpler and cheaper for a small or mid-sized company.

What local deployment actually means

A language model (LLM) is a program that understands text and replies to it. With the cloud, you send each request over the internet to a provider such as OpenAI or Anthropic. With local deployment, you download an openly available model and run it yourself. Requests never leave your network.

The most powerful (so-called frontier) models cannot be installed on your own server. They are available only as a service. Your server will run openly available models. According to PremAI, these come close to the best models in quality, but they are not equal to them. The same source says a smaller model fine-tuned for your specific tasks can beat a large general-purpose model on those tasks.

When local deployment pays off

PremAI is a vendor, and its blog lists the situations below. Treat the figures as indicative.

• Rules require local processing. Examples are air-gapped systems with no internet connection, healthcare or finance. If your lawyer tells you the data must not go to an external service, price comparisons become irrelevant.
• Volume is high and steady. According to PremAI, local deployment starts to beat cloud APIs above roughly 2 to 3 million tokens a day. A token is roughly a piece of a word, and it is the unit you are billed by. The volume must be steady over the long term, not just high once.
• Response time matters. PremAI says the cloud adds 50 to 200 ms, while local deployment can get under 20 ms. For ordinary office use this rarely decides anything.
• You already run GPU infrastructure. Adding one more workload is then cheaper than building from scratch.

The cloud wins when you need the most powerful models, when your load fluctuates or comes in bursts, and when your team has little capacity.

What it costs and when it pays back

PremAI estimates that a production deployment of a 70B-class model (70 billion parameters, so a large model) costs 40,000 to 190,000 USD upfront. That figure excludes operations, cooling, maintenance and staff. A study the blog cites (54 scenarios) reports payback against commercial APIs ranging from 18 months to 9 years.

It also depends on what you compare against. Against GPT-4, payback works out at roughly 2 million tokens a day and 70% GPU utilization. Against cheaper cloud models, such as Gemini 2.5 Pro or Claude Haiku, the blog estimates 5 years or more.

People are part of the cost too. According to PremAI, a production deployment needs at least one MLOps specialist, meaning someone who runs and maintains models. It puts the average US salary for the role at 135,000 USD a year. That person handles driver updates, security patches, swapping in newer models and fixing failures. GPU generations change every 2 to 3 years, and the blog recommends planning hardware renewal on a 3 to 5 year cycle.

What you need to have ready

Hardware

The deciding factor is graphics card memory (VRAM). A rule of thumb: the number of parameters in billions times 0.5 for 4-bit quantization, times 1.0 for 8-bit, and times 2.0 for FP16. Quantization shrinks a model at the price of a small loss of accuracy. A 70B model in its 4-bit version needs roughly 35 to 40 GB of VRAM.

More figures from PremAI:

• One card draws 300 to 700 W.
• A four-card node draws up to 3 to 4 kW.
• Budget another 20 to 30% for cooling.
• A CPU alone, without a GPU, manages 2 to 10 tokens per second on a 7B model, against 30 to 100 for a GPU. For real production use, that means a GPU.

Software

A card and a model file are not enough. You need a program that serves the model to many users at once. One option is vLLM, which PremAI describes as the highest-throughput tool for multiple users. It is an open-source library that began in the Sky Computing Lab at UC Berkeley and is now maintained by the community. It offers an OpenAI-compatible server, so existing tools can connect without changes. It supports NVIDIA and AMD cards, CPUs and, through plugins, other accelerators. It handles quantization, batching of many requests and splitting a model across several cards. According to its documentation, it works with more than 200 model architectures. PremAI also mentions Ollama, a simpler option for getting started.

Security

Local does not automatically mean secure. Responsibility moves from the provider to you. PremAI recommends role-based access control, multi-factor authentication for administrators, data encryption and logging of every request.

From test to production

PremAI recommends three phases:

1. Validation. Run your target model on a single developer machine. Compare the quality of its answers with what you use today.
2. Pilot. Deploy one powerful card and put real work on it. Measure speed and utilization, and calculate the true cost against your current cloud spend.
3. Production. Only now expand capacity and introduce full security, monitoring and documentation.

Questions to answer before you decide

If your answers split, a hybrid model fits. A steady base load runs locally, while peaks and the most powerful models run in the cloud. Sensitive data goes to the local server and routine requests go to the cloud.

Rules that apply wherever the AI runs

Local deployment does not remove your obligations. It only changes who fulfils them.

AI literacy. Under Article 4 of the EU AI Act (Regulation (EU) 2024/1689), providers and deployers of AI systems, meaning companies that use them, have been required since 2 February 2025 to take measures to improve AI literacy. This covers your employees and anyone using AI on your behalf, such as contractors. According to the wording quoted by the portal artificialintelligenceact.eu, you do not have to guarantee a specific level of knowledge for each individual. The Act does not prescribe the length, form or frequency of training. Kristýna Poche, a Czech lawyer writing on the Czech legal portal epravo.cz, regards the following as good practice: onboarding training for new staff, repeating it at least once a year, mapping the AI systems in use and their risks, and keeping training records. She also recommends covering literacy requirements in contracts with suppliers. According to the German portal KI-Campus, Article 4 stays binding even after the changes in the EU's AI Omnibus package, which only softens its wording.

Personal data. On 17 December 2024 the European Data Protection Board (EDPB) issued Opinion 28/2024. According to a summary by the law firm CMS, whether a model trained on personal data is anonymous is judged case by case, and the bar is high. When developing and deploying models, a controller can in principle rely on legitimate interest. It must, however, pass three steps: identify its interest, show necessity, and balance that interest against the rights of the people whose data it processes. If you feed employee or client data into a model or its knowledge base, run this test and write it down.

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Prepared by AI from the listed sources and checked automatically against them. Not legal advice.