Brain vs RTX and LLMs: A Computational Showdown

In the age of rapidly advancing technology, comparing the human brain to building blocks of a computer opens up an intriguing conversation. Both systems represent incredible feats of engineering—one through billions of years of evolution, the other through decades of technological innovation. 

This image  that I came across on reddit illustrates features of the brain vs a powerful graphics card like the NVIDIA RTX 4090. As of September 2024, the NVIDIA GeForce RTX 4090 remains one of the most powerful consumer GPUs available, especially for gaming and productivity tasks like rendering and AI workloads. It excels in 4K gaming and can handle even the most graphically demanding games, with exceptional performance in ray tracing and DLSS 3 technology, delivering up to 3-4x the performance of previous-generation GPUs like the RTX 3090 Ti. NVIDIA GeForce RTX 4090 is rumored to be  discontinued in October in preparation For Next-Gen RTX 5090 & 5090D GPUs, which could be up to 70% faster than the 4090, based on the new Blackwell architecture. Faster than numbers given for the brain in this image. This would be a massive leap in performance, but the estimate of 100 TFLOPS for the brain's compute power is a conceptual one, based on rough calculations of the number of neurons, synapses, and their firing rates, so the brain could be still superior. 

The human brain is a marvel of natural engineering. It is estimated that the brain can store between 10 and 100 terabytes of information, though the exact number is hard to pin down due to the complexity of neural storage. The brain operates on just 20 watts of power, making it incredibly energy-efficient compared to modern technology. Unlike silicon-based hardware, the brain is highly adaptable, capable of reconfiguring itself, learning from new inputs, and recovering from damage. The brain handles vast amounts of parallel processing, running thousands of operations simultaneously without us even realizing it.

The most striking difference between the two is energy efficiency. The brain operates on just 20 watts, performing around 5 times more operations per watt compared to the RTX 4090, which consumes 450 watts to push through its demanding tasks. This efficiency is one of the primary reasons scientists are fascinated by the brain's computational architecture—it offers clues to how we might one day build more power-efficient computers.

Moreover, the complexity of the brain’s network of neurons is far more sophisticated than the layout of a GPU. Neurons are interconnected in ways that allow for both logical and emotional processing, far beyond the binary calculations a GPU can perform.

Though graphics processors are an incredible feat of human engineering, nature still holds some of the most advanced "technologies" we know. The human brain is a highly energy-efficient, adaptable, and sophisticated processor, while the RTX 4090 shines in brute force computational tasks.

A high-end GPU like the RTX 4090 is specifically designed for high floating-point performance, which is a specific type of mathematical computation relevant particularly in graphics and scientific computing. Brain is a multi-tasker. 

Unlike computer random access memory (RAM), which is temporary and volatile, the brain's memory storage is not neatly divided and is highly integrated with processing functions.

Neurons are the brain's equivalent of electronic circuits, but they operate chemically and electrically rather than purely electrically as transistors do. If one were to make a comparison, a better analogy might be between synapses (the connections between neurons) and transistors, as synapses determine the paths of neural signals, somewhat akin to how transistors control current flow in circuits. Ion channels in neurons could be also likened to transistors in that they regulate the flow of ions (which carry electrical charge) across the neuron’s membrane, influencing the neuron’s ability to fire. The average number of ion channels on a neuronal membrane is estimated to be in the millions. 

The brain's ability to reorganize itself, forming new connections and strengthening or weakening existing ones in response to new information or injury, can be likened to software updates in computers that add features or fix bugs.

In artificial neural networks, weights are used to adjust the significance of inputs to neurons, similar to how synaptic efficacy is adjusted in biological neural networks based on experience.

Memory for Artificial Intelligence models LLMs refers to both short-term (RAM used during computation) and long-term (the model weights stored on disk). They do not "remember" past interactions in the way the brain does, unless explicitly designed to (as in recurrent neural networks). LLMs can utilize external memory for data handling, but a single model like GPT-4 may need several terabytes of storage to store its parameters. However, its working memory at inference is limited by the amount of VRAM on a GPU (e.g., 40 GB for NVIDIA A100 GPUs). The brain stores memories in a highly associative and distributed manner, unlike computers. It doesn’t just store data—it retrieves and processes information contextually, drawing from experiences, emotions, and sensory inputs simultaneously. The brain retains knowledge over a lifetime, dynamically updating memories and skills. 

LLMs learn during their training phase, where they are exposed to vast amounts of data. Once trained, their "knowledge" is fixed unless retrained with more data. They can’t adapt or self-learn in real-time without external intervention. However, they can adjust their output based on specific prompts or use fine-tuning to handle specialized tasks. The human brain can learn and adapt continuously, forming new synaptic connections and reinforcing old ones through a process called neuroplasticity. It learns from every experience and can generalize across vastly different contexts, which LLMs struggle with unless specifically trained on vast datasets covering many domains.

LMs like GPT-4 use a transformer architecture, which is designed to capture dependencies in language by processing sequences of tokens (words or characters) in parallel. Transformers excel at tasks requiring pattern recognition and have been trained on massive datasets (terabytes of text data), but they don't handle causality, real-time decision-making, or multi-sensory input the way the brain does. The brain processes data through complex networks of neurons that are wired together in specific regions for different functions. Sensory input, motor control, and cognitive processing happen simultaneously, in real-time, and across many regions that are interconnected. The brain can handle multimodal inputs (vision, hearing, touch, smell) and generate output (speech, movement) with amazing efficiency.

The human brain can generalize knowledge and apply learning across a wide range of contexts (transfer learning). LLMs struggle to generalize outside of the specific domain or dataset they were trained on without significant fine-tuning.

As we continue to explore artificial intelligence, neuroscience, and computational hardware, the brain could inspire new advancements in computing. Concepts like neuromorphic computing—where computer architectures mimic the structure of the brain—are already in development, suggesting that the future of technology may blur the line between biological and machine computation.