Running Local Large Language Models on Multi-GPU Clusters for Secure Legal Drafting
May 26, 2026 · Edited by Oleksandr Kuzmenko
An architecture pattern demonstrates how a cluster of 12 enterprise V100 GPUs can be networked together to run large-scale local LLMs for private document automation and drafting.
Why it matters
You can salvage older enterprise hardware to run ultra-large coding and reasoning models locally, avoiding cloud compliance issues and recurring token fees.
Key takeaways
- Network older enterprise GPUs via NVLink to aggregate VRAM for massive model sizes
- Deploy vLLM with tensor parallelism enabled to split model weights across multiple cards
- Run highly confidential document processing locally without relying on external cloud endpoints
Training or hosting complex models on modern Hopper or Ada Lovelace architectures is expensive. Many developers sit on older, enterprise-grade hardware but struggle to configure them for modern, ultra-low latency inference stacks. This setup shows how a cluster of 12 enterprise V100 32GB SXM2 GPUs can be chained together for highly specialized, ultra-private local legal drafting and code assistance.
By setting up TensorRT-LLM or vLLM over high-speed NVLink connectors on older SXM2 boards, you can pool massive amounts of VRAM (up to 384GB) to run ultra-large 70B+ parameter models at highly competitive context lengths. The setup uses tensor parallelism to distribute the weight matrix multiplications across all 12 cards, maintaining rapid response times despite the older tensor core generation.
If your agency handles highly sensitive legal documents or proprietary codebases, you can run Llama-3-70B on this local cluster. This allows you to feed entire 100-page contracts or large repositories into the context window without transmitting any data over the internet, while enjoying lightning-fast generations.
Power consumption on twelve enterprise cards can be massive, and setup complexity is high compared to standard consumer-grade Mac Studio configurations.
This setup represents a highly viable hardware recycling path for developers wanting absolute data privacy and giant local context sizes.
Source: Reddit · r/LocalLLaMA ↗