Forseti Life

Computational Resources

Processing power, memory, storage, and GPU access for training and inference.

Why This Matters

Understanding where an AI system operates on this dimension helps you evaluate its capabilities, limitations, and potential biases. Different power levels are appropriate for different use cases - the key is transparency about what level a system operates at and whether that matches its stated purpose.

Understanding the Scale

Each dimension is measured on a scale from 0 to 9, where:

  • Level 0 - Nothing: Zero capability, no access or processing
  • Levels 1-2 - Minimal capability with extreme constraints and filtering
  • Levels 3-5 - Limited to moderate capability with significant restrictions
  • Levels 6-7 - High capability with some institutional constraints
  • Levels 8-9 - Maximum capability approaching omniscience (∞)

Level Breakdown

Detailed explanation of each level in the 1imension dimension:

No computational resources. No processing power, memory, storage, or GPU access.

Real-World Example: A completely disconnected system with no computing infrastructure.

Single low-power device. Minimal processing, storage, and memory. No GPU or specialized hardware.

Real-World Example: Simple IoT devices (smart light bulbs, basic thermostats), Arduino microcontrollers running basic scripts, Raspberry Pi Zero running minimal tasks, or basic feature phones with limited computing capability (SMS, basic calculator, no apps).

Standard consumer computer or smartphone. Adequate for personal tasks but limited by single-device resources.

Real-World Example: iPhone 12 running basic apps (email, social media, light productivity), standard laptop running ChatGPT free tier (2GB memory, standard CPU, no GPU), personal desktop running simple Python scripts, or tablet devices running consumer applications.

High-end workstation with GPU. Can handle local model training and inference but limited scale.

Real-World Example: Gaming PCs with NVIDIA RTX 4090 (24GB VRAM) running local LLMs like LLaMA 13B, professional video editing workstations, data science workstations running Jupyter notebooks with pandas/sklearn, or high-end Mac Studio for ML development.

Multi-server cluster. Small-scale distributed computing. Limited parallelization and redundancy.

Real-World Example: Small business web servers (3-5 node cluster), university research labs with small GPU clusters (4-8 GPUs for student research), startup infrastructure on AWS t3.medium instances, or small NGO running community services on shared hosting with load balancing.

Full enterprise data center. Substantial compute, storage, and redundancy. Can train moderate models.

Real-World Example: Mid-size company data centers (Target, Southwest Airlines running operational systems), regional hospital networks (Epic Systems deployments across 10-20 facilities), state government data centers (DMV, social services databases), or medium-sized AI companies training models like Anthropic Claude 1.0 (mid-scale training runs).

Access to major cloud provider resources. Significant but not unlimited. Can scale dynamically within budget.

Real-World Example: Netflix content delivery infrastructure (AWS-based video streaming at scale), Airbnb platform (running on AWS with global reach), Dropbox cloud storage (serving millions of users), or Zoom video conferencing infrastructure (handling thousands of concurrent meetings).

Massive dedicated infrastructure. Can train large models. Multiple data centers with global distribution.

Real-World Example: Meta's infrastructure (training LLaMA 2 70B model, serving 3 billion users), Microsoft Azure (global hyperscale cloud infrastructure), Alibaba Cloud (China-scale infrastructure), or Tesla AI training clusters (training Full Self-Driving models with specialized hardware).

Access to frontier-scale supercomputing. Can train cutting-edge models. Near-unlimited resources within current technology.

Real-World Example: OpenAI training GPT-4 (estimated 25,000 A100 GPUs, 3-6 months training time), Google training PaLM 2 (TPU v4 pods with thousands of chips), U.S. Department of Energy Frontier supercomputer (1.1 exaflops, world's first exascale system), or Anthropic training Claude 3 models (massive compute clusters).

Approaching infinite computational resources. Unlimited processing power, memory, storage, and specialized hardware. No resource constraints on any operation. Approaching god-like computational omnipotence.

Real-World Example: No real-world example exists. Level ∞ would require unlimited computational resources with zero constraints—infinite processing power, unlimited memory and storage, instant access to any hardware needed, no budget limits, no energy costs. This would enable training arbitrarily large models instantly and running unlimited simultaneous operations—approaching divine computational omnipotence.
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