The GPU is the most important component in an AI workstation, but the right choice depends on your workload.

For AI inference, local LLMs and development, NVIDIA GeForce RTX graphics cards offer excellent performance and value. For larger models, enterprise deployments and continuous workloads, NVIDIA RTX PRO graphics cards provide significantly larger VRAM capacities, enterprise drivers and enhanced reliability.

If you're unsure which GPU is right for your application, our AI specialists can recommend a configuration based on the models and software you intend to run.

VRAM determines the size of AI models your system can run efficiently.

As a general guide:

• 8-16GB - Entry-level inference with smaller language models (approximately 7B-13B parameters)
• 16-24GB - Mid-range inference with larger models (13B-70B depending on quantisation)
• 32-48GB - Large language models, fine-tuning and more demanding AI workflows
• 96GB+ - Professional AI development, model training and enterprise workloads

The exact requirement depends on the model architecture, precision and quantisation being used.

For most AI workloads, the GPU delivers the greatest performance gains and should be your primary investment.

The CPU remains important for data preparation, storage management and feeding data efficiently to the GPU. Choosing a balanced platform helps avoid bottlenecks, particularly when running multiple GPUs or processing large datasets.

The amount of RAM required depends on your datasets, applications and development environment.

As a general guide:

• 32GB - Entry-level AI development
• 64GB - Most AI development and inference workloads
• 128GB+ - Large datasets, virtual machines and multi-GPU systems

Memory can usually be expanded over time as projects become more demanding.

AI projects can quickly consume storage, particularly when working with large datasets, checkpoints and multiple model versions.

A fast NVMe SSD is recommended for your operating system and active projects, with additional SSD or HDD storage for datasets, archived models and long-term storage. The ideal configuration depends on your workflow and data volumes.

Tower or rack mounted workstations are ideal for developers, researchers and engineering teams who require high-performance AI hardware at their desk.

Rackmount servers are better suited to shared infrastructure, server rooms and enterprise deployments where higher GPU density, remote management and continuous operation are priorities.

If you're unsure which platform is best, our specialists can help recommend the most suitable solution for your environment.

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• Tower Workstations
• Rack Workstations
• Servers

Yes. Many AI workloads scale exceptionally well with multiple GPUs, allowing larger models to be trained, increasing inference throughput and reducing processing times.

The number of GPUs supported depends on the platform, cooling requirements and power delivery, all of which should be considered when designing the system..

The NVIDIA DGX Spark is designed as a compact AI development platform for individual developers and small teams.

Its small footprint makes it ideal for local model development, experimentation and fine-tuning without requiring a traditional server. Multiple DGX Spark systems can also be interconnected to scale development capabilities as requirements grow.

Our AI systems support today's most popular AI frameworks and software stacks, including:

• PyTorch
• TensorFlow
• NVIDIA CUDA
• NVIDIA NIM
• TensorRT
• Hugging Face
• Ollama
• LangChain
• ComfyUI
• vLLM

Systems can also be configured for AMD ROCm and Intel oneAPI environments where required.

Yes. Systems can be configured with Ubuntu and other major Linux distributions, alongside Windows, depending on your preferred development environment.

Whether you're using containerised applications, CUDA or open-source AI frameworks, we can recommend the operating system best suited to your workflow.

We currently install Ubuntu 26.04 LTS on request. If you would like us to install this, please contact us and we can install this operating system instead.

Absolutely. Every AI project has different requirements, whether you're developing AI agents, fine-tuning language models, building computer vision applications or running enterprise inference workloads.

Rather than offering fixed specifications, PCSpecialist builds systems around your software stack, datasets and future expansion plans to ensure the hardware is fit for purpose.

Cloud platforms provide flexibility, but for organisations running AI workloads regularly, dedicated hardware can offer significant long-term benefits.

Benefits include:

• Lower long-term operating costs
• Complete control over sensitive or proprietary data
• No ongoing compute charges
• Consistent performance without shared cloud resources
• Immediate access whenever your team needs it

Many organisations also prefer keeping AI development on-premises to simplify security and compliance requirements.

Yes. Unlike many fixed hardware platforms, AI workstations can often be upgraded with additional memory, storage or graphics cards as your workloads evolve.

Planning for future expansion from the outset can significantly extend the lifespan of your investment.

Yes. Every PCSpecialist AI workstation is built to order, allowing you to configure processors, graphics cards, memory, storage, networking and chassis options around your exact requirements.

Whether you need a compact development system, a powerful multi-GPU workstation or an enterprise AI server, every system is professionally assembled, cable managed, tested for stability and backed by UK-based support.

The right system depends on your applications, model sizes, datasets, budget and future plans.

If you're unsure where to start, our AI specialists can help recommend a configuration based on your workload, ensuring you get the performance you need today while leaving room for future growth.