Hardware Division

Build Your
AI Sovereignty

Custom-engineered deep learning workstations.
Decoupled from the cloud. 40% more cost-efficient than AWS instances.

Zero Friction

The Neural Stack

Stop fighting dependencies. Our NeuraOS image comes pre-loaded with the entire modern AI toolchain.

  • Ubuntu Server 24.04 LTS
  • NVIDIA CUDA 12.4 + cuDNN
  • PyTorch 2.4 (Nightly)
  • Docker + NVIDIA Container Toolkit
admin@cortexa:~
admin@cortexa:~$ nvidia-smi --query-gpu=utilization
GPU 0: NVIDIA RTX 4090 ... 98%
GPU 1: NVIDIA RTX 4090 ... 99%
Memory-Usage: 23102MiB / 24564MiB
admin@cortexa:~$

Compute Infrastructure

Three tiers designed for every stage of model development, from fine-tuning to training.

Most Popular
Researcher Pro

Researcher Pro

$0

Max single-GPU performance. Perfect for individual researchers and quantization experiments.

  • 0 TFLOPS Compute
  • AMD Ryzen 9 9950X
  • NVIDIA RTX 4090 24GB
  • 0GB DDR5 6000MHz
Configure Unit
Best Value
Laboratory Edition

Laboratory Edition

$0

Dual RTX 4090 architecture. Built for parallel batch processing and 70B parameter model tuning.

  • 0 TFLOPS Compute
  • Dual NVIDIA RTX 4090
  • 0GB DDR5 6000MHz
  • 4TB NVMe + 8TB Archive
Configure Unit
Enterprise
Enterprise Cluster

Cluster Node

$0

Scalable 4x GPU compute nodes with 25G networking. Designed for rack mounting.

  • 0 TFLOPS Compute
  • 4x NVIDIA RTX 4090 (96GB VRAM)
  • 25G Networking Pre-installed
  • Ubuntu Server / Rocky Linux
Configure Unit

Inference Cost (Hourly)

Llama 3 70B (FP16)
AWS p4d
Lambda
Cortexa

On-premise hardware amortized over 3 years yields a 60% ROI compared to cloud rentals.

Capital Efficiency

Stop Renting.
Start Owning.

Cloud providers mark up GPU compute by 400%. By moving your training workloads to NeuraForge hardware, you reclaim your margins and gain data sovereignty.

Calculate Your ROI →

The NeuraForge Standard

Hardware engineered specifically for the modern LLM stack.

Model Agnostic

Decoupled from model providers. Run Llama 3, Mistral, or Flux locally without API costs or data leakage risks.

Memory Density

We standardize 128GB+ RAM to ensure CPU offloading is efficient when VRAM limits are reached during inference.

Ready to Deploy

Shipped with CUDA 12, PyTorch, and Docker pre-configured. Boot directly into your Jupyter Lab environment.

Capital Efficient

40% cheaper than equivalent Dell/HP workstations by utilizing consumer-grade flagships over overpriced Quadro cards.

Whisper Acoustics

Custom liquid loops designed for the office. Train 70B models at < 40dB—quieter than a library.

Enterprise Warranty

3-year advance replacement on all components. We swap parts first, ask questions later, maximizing uptime.

Initiate Procurement

Ready to build your custom AI Workstation? Request a formal quote for your finance department.

Request Quote đź“§