represents a major leap forward in large language model capabilities. Developed by OpenAI, it builds on the architecture and strengths of GPT-3 while achieving new levels of scale and performance. GPT-4 This article summarizes the based on currently available public information. key details about GPT-4 Model Stats ~1.8 trillion parameters (over 10x more than GPT-3) Total parameters: Uses a mixture of experts (MoE) model to improve scalability Architecture: Trained on ~25,000 Nvidia A100 GPUs over 90-100 days Training compute: Trained on a dataset of ~13 trillion tokens Training data: Supports up to 32,000 tokens of context Context length: Model Card The GPT-4 model card provides transparency into the model's training data, intended uses, capabilities, limitations and more. Transformer with Mixture-of-Experts Model type: Web text, books, Wikipedia, Reddit, Amazon reviews Training data: Text generation, QA, classification, conversational agents Intended uses: Text generation, QA, classification Capabilities: Text Modalities: Potential for bias, harmful outputs, misuse Ethical considerations: Lack of grounded reasoning, factually incorrect outputs Limitations: Model Architecture GPT-4 utilizes a architecture with separate expert neural networks that specialize in certain tasks or data types. mixture of experts (MoE) 16 expert models, each with ~111B parameters 2 experts activated per inference query 55B shared parameters for attention ~280B parameters used per inference pass This allows the overall model to scale up while keeping inference costs practical. The specialized experts can also develop unique capabilities. Training Process Training a 1.8 trillion parameter model required extensive computational resources: Trained on ~25,000 Nvidia A100 GPUs simultaneously 90-100 days of continuous training 13 trillion training tokens 2.15e25 floating point operations (FLOPs) total Various parallelism techniques enabled this scale: 8-way tensor parallelism 15-way pipeline parallelism Clustering topologies to maximize inter-GPU bandwidth Inference Serving Deploying GPT-4 also requires specialized infrastructure: Runs on clusters of 128 A100 GPUs Leverages 8-way tensor and 16-way pipeline parallelism Carefully balances latency, throughput, and utilization Uses speculative decoding to improve throughput Dense inference clusters keep query costs affordable at scale. Token Dropping The MoE routing mechanism can lead to , where some tokens are unprocessed due to expert capacity limits. token dropping Drops are non-deterministic based on batch token routing Some level of dropping is beneficial for efficiency Varying drops lead to observed randomness, but model logic is consistent Future Directions While impressive, GPT-4 remains focused on text. Future areas of research include: Architectures supporting vision, audio, speech Training across modalities Alternatives to MoE for scalability Expanding training data diversity and size Advancing multi-modal reasoning Optimizing for real-world performance GPT-4 demonstrates the rapid pace of progress in language models. While we are still far from general intelligence, OpenAI continues pushing towards this goal with each new iteration. Exciting capabilities likely lie ahead.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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