NVIDIA GPU Workstations for Local LLM

BIZON GPU workstations are optimized for local LLM inference and training — no cloud, no API costs, full data privacy. Run Gemma, Llama, NVIDIA Nemotron, Kimi K, DeepSeek, Qwen, Mistral Large, GLM-5, Phi-4, MiniMax, MiMo LLMs, and other open-source models out of the box with pre-installed vLLM, Ollama.

From 7B models on a single GPU to 1T-parameter models on 8× NVIDIA RTX PRO 6000 96GB Blackwell — plug in and start inference.

Every prompt, every response, every document you feed into your model stays on your machine. No cloud provider reads your data, no third-party retention policies apply. Whether you're a startup protecting IP, a law firm handling privileged information, or a developer who simply doesn't want their code on someone else's server — local is the only way to guarantee privacy.

• Which BIZON workstation is right for me?

  It depends on the models you want to run. For 7B–14B models a single-GPU system is plenty. For 70B+ models you'll want dual or quad-GPU. Not sure? Tell us your use case and our engineers will recommend the right config.

• What GPU do I need to run a 70B model locally?

  At Q4 quantization, a 70B model needs ~40 GB VRAM. A dual RTX 5090 (64 GB total) handles this comfortably. For unquantized or 200B+ models, RTX PRO 6000 Blackwell cards offer 96 GB each.

• How does this compare to a Mac Studio / Mac mini?

  Apple Silicon offers large unified memory but ~600 GB/s bandwidth. A single RTX 5090 delivers 1,792 GB/s — roughly 9x faster. For inference speed, dedicated NVIDIA GPUs win decisively.

• What software comes pre-installed?

  Ubuntu or Windows with Ollama, LM Studio, Docker, CUDA, and cuDNN. Power on, pull a model, go. Can I run multiple models at once? Yes. Multi-GPU systems can serve different models on different GPUs simultaneously — a coding assistant on one, a document summarizer on another.