Hardware Built for AI
Purpose-built workstations and edge devices optimised for running local language models, agents, and image generation at scale.
Starter: Lightweight & Mobile
Ideal for individual developers, consultants, and teams piloting AI workflows.
HP ZBook Firefly 14 G11
Portable, power-efficient developer laptop.
- Intel Core i7/i9, 16–32GB RAM
- NVIDIA RTX 4050 (6GB VRAM)
- 512GB – 1TB NVMe SSD
- 14-hour battery life
- 1.5kg, fits any backpack
- Excellent thermal design
From £1,200
Mac Studio (M2 Max)
For teams already in the Apple ecosystem.
- M2 Max, 32–64GB unified memory
- Integrated 19-core GPU
- Exceptional power efficiency
- 512GB – 2TB SSD
- Great for fine-tuning small models
- Seamless with macOS tooling
From £1,800
NVIDIA Jetson Orin NX
Tiny, powerful, for edge deployment.
- 8-core ARM processor
- 100 TFLOPS AI compute
- 8–16GB LPDDR5 memory
- <10W power draw
- Perfect for robots, drones, IoT
- Industrial-grade reliability
From £200
Workstation: Production-Grade Power
For teams running multiple models, fine-tuning, and image generation simultaneously.
HP ZBook Fury 16 G11
Workstation laptop for demanding workflows.
- Intel Xeon or Core i9, up to 96GB RAM
- NVIDIA RTX 6000 Ada (48GB VRAM)
- 2–4TB NVMe SSD (RAID capable)
- 16" 4K display, 100% DCI-P3
- Dual Thunderbolt, multiple USB-C
- ISV-certified for CAD, rendering
From £3,500
Dell Precision 7960
Desktop workstation for the lab or office.
- Dual Intel Xeon Platinum, 384GB+ RAM
- Up to 4× NVIDIA RTX 6000 Ada
- 8TB+ NVMe arrays, redundancy
- Dedicated power (1600W PSU)
- Liquid cooling options
- 200+ TFLOPS per unit
From £8,000
NVIDIA RTX Workstations
Partner pre-builds optimised for AI.
- RTX 4090 or RTX 6000 Ada
- Ryzen 9 or Xeon configurations
- Up to 192GB DDR5 memory
- Proven Docker compatibility
- Pre-tested with Ollama, ComfyUI
- Extended warranty options
From £2,500
GPU Cabinet: Enterprise Scale
Run 100+ concurrent agents, serve unlimited models, handle peak load without scaling down.
Multi-GPU Rack Setup
- 4–8 NVIDIA H100/RTX 6000 Ada cards
- 1–2 PB/s interconnect bandwidth
- 576GB– 1.4TB GPU memory total
- Liquid cooling, redundant power
- 500+ TFLOPS aggregate
- Isolated virtual machine per tenant
Typical Use Cases
- Parallel multi-agent orchestration
- Batch image generation (1000s/day)
- Model fine-tuning at scale
- Vector DB with 10M+ embeddings
- Real-time inference SLAs
- Multi-department shared resource
Cabinet starting at £45,000. Includes installation, networking, and 6-month support. Ask about financing options.
Edge Devices: Distributed Intelligence
Deploy AI models across your field workforce, factories, and remote locations.
NVIDIA Jetson Orin AGX
Most powerful edge AI module available.
- 275 TFLOPS peak performance
- 64GB LPDDR5X memory
- 12-core ARM Cortex processor
- <60W power consumption
- Autonomous systems, robotics
- PCIe, USB-C, Ethernet
From £500
Raspberry Pi 5 (Industrial)
Low-cost, high-reliability edge compute.
- 64-bit ARM, 8GB LPDDR5
- PCIe Gen3 NVMe interface
- Fanless, <10W typical
- Secure boot, TPM 2.0
- Cost-effective fleet deployments
- Industrial-grade housing available
From £75
Custom Edge Kits
Bespoke configurations for your use case.
- Jetson + sensors + power management
- 5G/LTE modem options
- Battery & solar integration
- Rugged enclosures, IP65/IP67
- Pre-configured with your models
- Rapid deployment support
Custom quote
Powered by HP Amplify
PrivateClaw partners with HP Amplify for hardware procurement, configuration, and ongoing support. Access volume pricing, extended warranties, and priority fulfillment through our partnership.
Hybrid: Local + Cloud
Not ready to buy hardware? Use RunPod for on-demand GPU compute alongside your local models. Scale elastically during peak load, keep sensitive workloads on-premise.
- • Spot pricing for batch jobs and training
- • Secure tunnels back to your on-premise LLMs
- • Pay-per-minute, no long-term commitment
- • A100s, H100s, RTX 6000 Ada available globally
Configure Your Rig
Need help choosing the right hardware for your workload? Let’s talk through your use case.