By VRLA Tech  ·  AI Computing  ·  April 2026

A local LLM server gives your team access to private, fast, unlimited LLM inference on your own hardware. No API costs, no data leaving your network, no rate limits. Building one in 2026 is simpler than most developers expect. This guide covers the hardware selection, software stack, and configuration steps for a production local LLM server.

Step 1: Choose your hardware

Hardware selection starts with your target model and concurrent user count. The GPU VRAM determines which model you can run. The GPU count and system RAM determine how many users you can serve simultaneously.

For a team of 5–20 users on 7B–13B models: Single NVIDIA RTX 5090 (32GB) workstation. Runs LLaMA 3 8B or 13B at FP16 with vLLM, serving 10–30 concurrent users depending on context length.

For a team of 10–50 users on 70B models: VRLA Tech 4-GPU EPYC LLM server with 384GB combined VRAM. Runs LLaMA 3 70B at FP8 with meaningful KV cache headroom for production concurrency.

For enterprise use (50+ users, 70B+): VRLA Tech 8-GPU EPYC server with 768GB combined VRAM.

Step 2: Install the software stack

The minimal software stack for a local LLM server: Ubuntu 22.04 or 24.04 LTS, NVIDIA drivers (current stable release), CUDA toolkit (12.x), and one of vLLM or Ollama as the inference server. VRLA Tech installs and validates this stack before shipping.

For production serving with an OpenAI-compatible API: install vLLM with pip install vllm, then start the server with vllm serve <model-name> --host 0.0.0.0 --port 8000. The server is now accessible from any machine on your network at http://<server-ip>:8000/v1.

For developer-friendly setup with automatic model management: install Ollama, run ollama serve as a background service, and pull models with ollama pull llama3. Ollama exposes a compatible API at port 11434.

Step 3: Configure client access

Any application that uses OpenAI’s Python client or HTTP API works with a local vLLM or Ollama server by changing one line: the base URL from OpenAI’s servers to your local server IP. In Python: client = OpenAI(base_url="http://<server-ip>:8000/v1", api_key="not-needed"). Everything else — chat completions, streaming, function calling — works identically.

Step 4: Monitor and optimize

Monitor GPU utilization with nvidia-smi or the vLLM metrics endpoint. If GPU utilization is consistently below 80%, you may have headroom to increase batch size or serve a larger model. If requests queue and latency rises under load, consider a second GPU or a larger multi-GPU server.

Browse local LLM server hardware on the VRLA Tech LLM Server page and the 4-GPU EPYC LLM Server page.

VRLA Tech has been building custom workstations since 2016. All systems ship with a 3-year parts warranty and lifetime US-based engineer support.