LLM Development Workstation
Maximum VRAM and memory bandwidth for building and serving large language models locally.
We help you choose, source, and procure the right infrastructure — no obligation.
Configuration at a Glance
Tailored per engagement. Full technical overview below.
Overview
The LLM Development Workstation is specified for teams working hands-on with large language models — fine-tuning, evaluation, and local serving. Nexus Compute prioritizes GPU VRAM and memory bandwidth in this configuration so you can work with the largest models a workstation can practically hold.
Who This Solution Is For
Business Benefits
Work with larger models locally
High-VRAM configuration runs models that would otherwise require cloud GPUs, keeping prompts and data private.
Faster prompt iteration
Local inference removes network latency and per-token cloud costs during development.
Private by default
Proprietary prompts, fine-tuning data, and model weights stay inside your environment.
Specified for LLM work
We bias the configuration toward VRAM and bandwidth — the factors that matter most for language models.
Typical Business Use Cases
Fine-tuning open-weight LLMs with LoRA/QLoRA
Local inference and serving for development and staging
RAG pipeline and vector database development
Agentic system prototyping with long context windows
Industry Applications
Technical Overview
A high-VRAM configuration built around dual RTX 5090 or RTX PRO 6000 GPUs, large ECC system memory, and fast NVMe for model weight storage. We tune the exact GPU choice to the model sizes you intend to run.
| GPU Options | Dual RTX 5090 (64GB) or RTX PRO 6000 (96GB) |
| CPU | AMD Threadripper PRO |
| System Memory | 256GB–512GB DDR5 ECC |
| Storage | 8TB+ NVMe for model weights |
| Software | vLLM, Ollama, Hugging Face stack pre-configured |
| Operating System | Ubuntu 22.04 LTS |
| Warranty | 3-year on-site, next-business-day |
Specifications are indicative and configured to each engagement. Request a quote for a configuration tailored to your requirements.
Frequently Asked Questions
How large a model can I run?
It depends on the GPU configuration and quantization. We size the VRAM to the models you intend to run and advise on what is realistic for full-precision versus quantized inference.
Which inference framework do you install?
We commonly pre-configure vLLM, Ollama, and the Hugging Face stack, but we install whatever your team standardizes on.
When should I move from a workstation to a server?
When you need to serve models to many concurrent users in production, or train at a scale beyond a single machine. We can advise on the transition to our GPU Server line.
Procurement Assistance
Source the LLM Development Workstation with Nexus Compute
Tell us your requirements and a procurement specialist will help you specify, source, and quote the right configuration — typically within two business days. No obligation.
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