Harness massive VRAM to fine-tune and run large language models locally. For sovereign setups using Hugging Face, vLLM, and Ollama.
Choose from our high-performance systems designed specifically for compute-heavy AI workloads.
Discover our dedicated workstations for NLP, Data Science, and Local Inference. We build specialized hardware architectures for every unique AI workload.
Accelerate model training, image processing, and real-time vision with powerful, GPU-optimized workstations built for AI workloads.
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Optimized for data processing, model development, and predictive analytics. Engineered for large-dataset throughput and rapid feature engineering workflows.
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Ideal for developers building, testing, and deploying AI models locally. Low-latency inference configurations with developer-friendly pre-configured environments.
Explore More →Explore Pro Maestro Servers, if your workload requires scaling beyond a dedicated workstation. We engineer these rack-optimized systems specifically for massive enterprise deployments and heavy-duty data processing.
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Find quick answers to common questions about our AI workstations, components, and performance capabilities.
The most critical hardware requirements for LLM workstations revolve around VRAM and memory bandwidth. To run models like Llama 3 or Mistral locally, you need enough GPU memory to hold the model parameters and the KV cache. We recommend at least 24GB of VRAM for 7B or 14B models, while larger models require a multi GPU AI workstation for large language models to avoid slow CPU offloading.
