Search Results: TransformerArchitecture

A deep dive into Andrej Karpathy's landmark 2015 RNN demonstration reveals striking parallels and critical divergences from modern transformer architectures. By reimplementing the byte-level Shakespeare generator in PyTorch, we uncover why fixed-context bottlenecks doomed RNNs while attention mechanisms unlocked the LLM revolution.

Transformer-based LLMs consistently become the performance choke point in production systems, with latency often exceeding user expectations by 10x. This deep dive reveals how the architecture's design for parallel training clashes with the sequential demands of token generation, creating fundamental bottlenecks rooted in memory access patterns rather than raw compute power. Understanding these constraints is essential for effective optimization.

Google Research introduces SLED, a novel decoding technique that improves LLM factuality by leveraging outputs from all transformer layers. The method reduces hallucinations by 16% on benchmarks without external data or fine-tuning, offering a lightweight solution to AI's accuracy crisis.

OpenAI's first open-weight LLMs since GPT-2, gpt-oss-120b and gpt-oss-20b, reveal strategic shifts in transformer design—embracing Mixture-of-Experts, MXFP4 quantization, and sliding window attention. We dissect how these choices stack against Alibaba's Qwen3 and what they signal for efficient, locally deployable AI. Source analysis shows surprising trade-offs in width vs. depth and expert specialization that redefine developer possibilities.