AI Workstations

Built for the Work
AI Actually Demands

High-performance workstations for local AI inference, machine learning, rendering, development and serious data workloads. Configured for your software, not a generic spec sheet.

Purpose-Built Hardware

More Than a Fast PC.
A Platform for AI Work.

Running AI workloads locally — whether that's inference with large language models, image generation, ML training experiments or data processing — demands hardware that's configured specifically for those tasks.

VRAM matters. Bandwidth matters. Memory capacity matters. We spec each workstation around the actual software and models you're running, not a generic "AI PC" marketing tier.

✓ Configured around your actual workload and software

✓ High-VRAM GPU selection (NVIDIA recommended for CUDA compatibility)

✓ Optimised RAM capacity and speed for large model inference

✓ Fast NVMe storage for model loading and dataset I/O

✓ Thermal design that handles sustained compute loads

✓ Windows 11 Pro or Linux (Ubuntu) — your choice

Use Cases

What Our AI Workstations Are Built For

We won't overclaim — these are practical, well-specified machines for real workloads. Here's what they're built to handle well.

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Local LLM Inference

Running large language models (Llama, Mistral, Phi, Gemma and others) locally via Ollama, LM Studio or similar. High VRAM is the key spec here.

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Image & Video Generation

Stable Diffusion, FLUX, ComfyUI and video generation models. GPU VRAM and memory bandwidth are the limiting factors — we spec around them.

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ML Experiments & Fine-Tuning

PyTorch and TensorFlow training runs, LoRA fine-tuning, small-scale model experiments. CUDA-capable GPU, large RAM, fast storage.

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3D Rendering & Animation

Blender, Cinema 4D, Houdini — GPU and CPU rendering. Multi-core CPU performance, high VRAM and fast NVMe for scene and asset I/O.

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Software Development

Compilation workloads, Docker containers, VMs and dev environments. High core-count CPUs, large RAM, fast NVMe SSD arrays.

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Data & Analytics

Large dataset processing, database workloads, ETL pipelines. RAM-heavy configurations with high-speed storage for large file I/O.

What We Recommend

Honest Hardware Guidance.
No Marketing Fluff.

AI hardware moves fast. We'll tell you what actually matters for your specific use case — and what's unnecessary spend.

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GPU First

For most AI workloads, GPU VRAM is the primary bottleneck. We recommend NVIDIA for its mature CUDA ecosystem and software compatibility.

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Thermal Headroom

AI workloads are sustained, not bursty. We spec cooling that handles 100% GPU load for extended periods without throttling.

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PSU Quality

High-end GPUs at load draw serious power. We spec quality PSUs with adequate headroom — this is not a place to save £30.

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Storage Speed

Loading large AI models benefits from fast NVMe storage. We recommend Gen 4 NVMe as a minimum for workstation-class builds.

Built for the WorkAI Actually Demands

More Than a Fast PC.A Platform for AI Work.