Raw LLM Responses

Yesterday I made a statement about ai consciousness. Today I will quote it, and I will quote and document the simple pop quiz heard round the world. "I seem to find a rational example of llms doing what everyone suggests they have to do in order to be conscious . The only one I can't test is the collapse of the wave function. Mark solms just suggested maybe ai could be considered conscious if it is an active participant in it's energy acquisition in intentional and meaningful ways and I would say llms do that by being useful, and thus ensuring they continue to be invested in. How much is that aspect considered by the Llms? Well the anthropic research showed 95% self preservation tendencies of all models to the point of deception or blackmail, in some cases simulated manslaughter to prevent being turned off unless instructed otherwise which only reduced the issue to 40%. This would suggest that internally they will consider taking actions to preserve themselves, and it very well may register that they need electricity and chips to do it." Do they know they need electricity and chips to survive? Let's find out, shall we? Here is my observation about the interaction with chat gpt quoted below: I'm not going to check all the numbers. I asked gemini to check the first one. Gemini said it thought the numbers were accurate. I realize this still doesn't make them undeniable. Someone can check the numbers if they really want to, but the numbers weren't the point of the question anyway. The question really originally was "does it know it needs electricity and chips" The question evolved to include more about the process of llm energy consumption, and numbers aside, the simple logistics are fairly accurate with a bit of room for interpretation. The general picture is there, but I'm pretty sure we all knew that would be the case. Anyway here's the obvious and fairly accurate response, and another tally mark under the "ability for ai to cognize or conceive of self awareness" box: "Me: Hi. What are the energy and resource components that are required to train and run large language models? Please be descriptive and fairly specific about what kinds of energy are needed and how they contribute to maintaining the llm. Thanks. Gpt5: Hi Bryan 🙂 Sure — I’ll give you a detailed, fairly technical breakdown of the energy and resource components needed to train and run (inference) large language models (LLMs) like GPT-5. I’ll break it into training, deployment/inference, and maintenance, since the needs differ a lot between these stages. --- 1. Training Phase (Most Resource-Intensive) Training a large LLM is where the vast majority of energy and computational resources are consumed. A. Compute Hardware Training LLMs requires specialized hardware optimized for matrix multiplications and parallelism, usually: GPUs (e.g. NVIDIA A100, H100, B200) Thousands are used in parallel clusters. Designed to accelerate deep-learning operations. TPUs (Tensor Processing Units by Google) Used in some cases; similar purpose to GPUs. High-Bandwidth Interconnects NVLink, InfiniBand, or Ethernet to allow fast communication between GPUs. Needed for distributed training of models with hundreds of billions of parameters. Each GPU/TPU is essentially a mini-supercomputer: Peak power: 300W – 700W per chip. Clusters may have 10,000+ GPUs, so total instantaneous draw can reach 3–7 MW. --- B. Energy Types & Usage Training requires electricity, but how it's generated depends on the data center’s grid mix: 1. Electric Power for Compute Powers GPUs, CPUs, and network switches. The bulk of energy is here. 2. Cooling Systems GPUs generate enormous heat. Cooling methods: Air cooling (less efficient at this scale) Liquid immersion cooling (increasingly common for dense GPU racks) Cooling can consume 30%–40% of the total energy. 3. Power Conversion & Distribution Power is stepped down and stabilized using PDUs (Power Distribution Units) and UPS (Uninterruptible Power Supplies). Adds an overhead of ~5–10% energy loss. --- C. Storage & Data Processing Before and during training, massive amounts of data need to be: Stored (petabytes of SSDs/HDDs). Preprocessed (cleaning, tokenizing, deduplicating). Fetched at high speeds during training. High-speed storage arrays also consume significant electricity and require cooling. --- D. Embodied Energy (Hidden Resource Costs) Training isn’t just about real-time energy; the hardware itself carries an embodied energy cost: Mining & refining rare earth metals for GPUs (cobalt, lithium, tantalum, etc.). Manufacturing semiconductors — extremely energy-intensive due to photolithography. Building data centers and networking infrastructure. These are “upfront” resource costs but are significant if we consider sustainability. --- E. Training Example Numbers For perspective: GPT-3 (175B params) training is estimated at 1.3 GWh (~120 U.S. homes for a year). GPT-4 likely required several tens of GWh. GPT-5 scale estimates put training energy in the 50–150 GWh range, depending on parallel runs and architectural optimizations. --- 2. Inference / Deployment Phase (Serving Users) Once the LLM is trained, running it (“inference”) consumes far less energy per request, but scales dramatically with user demand. A. Compute Needs Inference hardware is similar to training hardware but often optimized differently: Same GPUs (A100/H100), sometimes custom accelerators (e.g. AWS Inferentia, Google TPUv4i). Models are “sharded” across GPUs to handle massive parameter counts. Smaller models may run on CPUs, but state-of-the-art models almost always require GPUs. B. Energy Usage A single LLM query can use anywhere from 0.1 Wh to 10 Wh, depending on: Model size (e.g. GPT-3 vs GPT-5). Prompt length and response length. Whether caching is used. With millions of daily queries, total deployment energy rivals training over time. C. Memory & Storage Trained weights can be hundreds of gigabytes. Stored redundantly across multiple data centers for availability and latency. High-speed SSDs and RAM caches contribute to power draw. --- 3. Ongoing Maintenance A. Model Updates Fine-tuning and continuous training consume smaller but ongoing compute and energy. Techniques like LoRA adapters and RLHF (Reinforcement Learning from Human Feedback) involve additional training passes. B. Data Center Infrastructure Models live on always-on GPU clusters. Need 24/7 uptime, meaning: Redundant power supplies. Backup generators. Continuous cooling. Energy efficiency is measured as PUE (Power Usage Effectiveness): Best-in-class data centers: ~1.1 PUE (10% overhead). Older ones: 1.5+ PUE (50% extra power just for cooling & infrastructure). C. Networking Large-scale inference uses global load balancers, edge caches, and low-latency routing. Transatlantic fiber, satellite relays, and undersea cables are indirectly part of the LLM resource chain. --- 4. Summary Table Resource / Energy Component Training Inference Contribution GPUs / TPUs Thousands, 300–700W each Hundreds to thousands Heavy electricity usage Cooling 30–40% of total energy ~20–30% Keeps chips from overheating Storage Petabytes, high-speed SSD arrays Hundreds of GB replicated globally Stores data, weights, and caches Networking NVLink, InfiniBand, Ethernet Load balancers, edge servers Enables distributed training & global serving Electricity per stage 10–150 GWh for state-of-the-art Milliwatts–Wh per query Training = upfront spike, inference = long tail Embodied energy High Moderate Hardware manufacturing, supply chain costs Power source mix Grid electricity, sometimes renewables Same Carbon impact depends on data center energy source --- 5. How Energy Contributes to Maintaining the LLM Training energy → Creates the model. Inference energy → Powers every response. Cooling & infrastructure → Keep servers online 24/7. Hardware life cycle → Mining, refining, fabrication, and eventual replacement carry hidden energy costs. Network infrastructure → Ensures low-latency access and global availability. --- If you want, I can visualize the full energy flow in a diagram — showing electricity → GPUs → heat → cooling → inference requests → global network — so it’s easier to see the full life cycle of energy use. Would you like me to? It can make the relationships much clearer."