AI & HPC Large Language Models

At Workstation PC, our rackmount AI servers are engineered for high-performance LLM training and inference, offering massive GPU memory capacities to handle the most demanding AI workloads. Built with multi-GPU configurations, powerful CPUs, and high-speed NVMe storage, these systems accelerate natural language processing, deep learning, and real-time AI applications. Whether you're fine-tuning models, running large-scale inference, or pushing AI research forward, our LLM-optimized servers deliver unmatched power, efficiency, and scalability.

Large Language Model Quad GPU Server

$8,499.00
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Designed for AI researchers and data scientists, this compact 2U rackmount server supports up to four NVIDIA GPUs, making it ideal for fine-tuning and inference with large language models. With support for NVIDIA RTX Ada and L40S graphics cards, it delivers the power needed for demanding AI workloads.

Featuring up to 192GB of combined VRAM, this system is optimized for 70B parameter FP16 inference and fine-tuning smaller models. It requires two power connections on separate circuits, with 240V power necessary for PSU redundancy, ensuring stability and reliability for intensive AI processing.
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Large Language Model 8 GPU Server

$14,179.00
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Built for large-scale AI workloads, this powerful 4U rackmount server supports up to eight NVIDIA GPUs, making it ideal for training, fine-tuning, and inference with advanced large language models. Designed for high-performance computing, it supports NVIDIA RTX Ada, L40S, and H100 NVL graphics cards to handle the most demanding AI tasks.

With up to 752GB of combined VRAM, this system is optimized for 150B parameter FP16 inference and fine-tuning smaller models. It requires four 200-240V power connections on separate circuits, ensuring the stability and redundancy needed for continuous AI model development and deployment.
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Large Language Model

Large Language Models (LLMs) are at the forefront of AI innovation, powering natural language processing (NLP), generative AI, and deep learning applications across industries. Training and fine-tuning these models require high-performance computing infrastructure with multi-GPU configurations, high-core-count CPUs, and massive memory bandwidth to handle billions of parameters and petabytes of data. At Workstation PC, we build LLM-optimized AI servers designed for scalable training, ultra-fast inference, and real-time deployment, ensuring researchers, developers, and enterprises can push the limits of AI without hardware constraints. Whether you’re working on transformer-based architectures, AI-driven automation, or large-scale conversational models, our precision-engineered servers provide the power, efficiency, and reliability needed to keep AI innovation moving forward.

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Workstation Hardware Guide

Large Language Model Workstation Guide: Performance & Recommendations

AI-driven Large Language Models (LLMs) are revolutionizing natural language processing, generative AI, and machine learning applications. Training and hosting these models require powerful multi-GPU servers, high-core-count CPUs, massive memory capacity, and ultra-fast storage. At Workstation PC, we build LLM-optimized AI servers engineered for scalable training, high-speed inference, and enterprise-level AI deployment, ensuring you stay ahead in AI innovation.

Processor (CPU)

What is the Best CPU for Large Language Model Servers?

For LLM servers, the platform is more critical than the specific CPU. We recommend:

  • AMD EPYC 9754 (128 cores) – High-density AI workloads and maximum PCIe bandwidth.
  • Intel Xeon® Platinum 8592+ (64 cores) – Optimized for multi-GPU AI applications.
  • AMD Threadripper PRO 7970X (32 cores) – Excellent for hybrid AI and data preprocessing workloads.

Do More CPU Cores Improve LLM Performance?

For inference and training, GPU power is the primary driver. However, high-core-count CPUs are beneficial for data preprocessing, embeddings, and vector search operations that run alongside AI models.

Do LLMs Work Better on Intel or AMD CPUs?

Both platforms provide excellent performance, but Intel Xeon offers oneAPI optimizations, while AMD EPYC provides higher core densities and PCIe lanes for multi-GPU setups.

Video Card (GPU)

How Does GPU Acceleration Impact LLM Performance?

LLMs rely on GPU compute power for model training, inference, and multi-user deployments. The amount of VRAM per GPU directly determines model size and batch processing efficiency.

What is the Best GPU for LLM Servers?

For professional-grade AI computing, we recommend:

  • NVIDIA H100 (80GB HBM3) – Best for large-scale LLM training and enterprise AI workloads.
  • NVIDIA RTX 6000 Ada (48GB VRAM) – Ideal for multi-user LLM hosting and fine-tuning.
  • NVIDIA L40S (48GB VRAM) – Great for AI inference and mid-scale LLM deployments.

Do Large Language Models Require Multiple GPUs?

Yes! LLMs scale efficiently across 4 to 8 GPUs, reducing training time and improving inference speed. Multi-GPU configurations allow parameter sharding across memory pools for handling billion-parameter models.

Do LLMs Run Better on NVIDIA or AMD GPUs?

NVIDIA remains the leader in AI acceleration with CUDA, TensorRT, and Tensor Cores. However, AMD’s MI300X GPUs with ROCm support are becoming a competitive alternative for open-source LLM frameworks.

Do LLM Servers Need NVLink?

For select models like NVIDIA H100 NVL, NVLink enables high-bandwidth GPU communication, improving efficiency for transformers, RNNs, and multi-node AI applications.

Memory (RAM)

How Much RAM Do LLM Servers Need?

For AI workloads, system memory should be at least 2× the total GPU VRAM. Our recommendations:

  • 512GB RAM – Suitable for mid-range LLM inference.
  • 1TB+ RAM – Ideal for multi-user AI servers and distributed training.
  • 2TB+ RAM – Required for enterprise AI clusters and massive dataset processing.

Storage (Drives)

What is the Best Storage Setup for LLM Hosting?

High-speed NVMe SSDs are essential for storing AI models, datasets, and rapid query processing. We recommend:

  • Primary Drive (OS & AI Frameworks): 2TB NVMe SSD for fast boot and software execution.
  • Model Storage (LLM Weights & Parameters): 4TB+ NVMe SSD for loading large AI models efficiently.
  • Data Pipeline Drive: 8TB+ NVMe SSD for handling real-time data ingestion and vector search.

Should LLM Servers Use Network-Attached Storage (NAS)?

For distributed AI workloads, network-attached storage with 10GbE+ networking is useful for backups, dataset sharing, and AI collaboration.

Get a Workstation Built for Large Language Models

At Workstation PC, we design high-performance AI servers tailored for LLM training, inference, and deployment. Whether you’re running multi-user chatbots, large-scale AI research, or enterprise NLP applications, our custom-built servers provide unparalleled power, reliability, and scalability.

Need Help Choosing the Right LLM Server?

Our experts can customize a build based on your AI model size, dataset requirements, and scaling needs. Contact us today for a free consultation!

Why Choose Workstation PC?

✅ Optimized for AI & LLMs – Tuned for large-scale AI workloads and NLP models.
✅ Certified AI Hardware – We use NVIDIA, AMD, and Intel AI-approved components.
✅ No Gimmicks – Just Performance – No overclocking, no shortcuts—just reliability.
✅ Expert Support – We understand AI workflows and enterprise LLM deployments.

🚀 Upgrade your AI infrastructure with a Workstation PC LLM server today!