LLM Internals Hub

OpenAI co-founder Ilya Sutskever declares the 'Age of Scaling' is over in exclusive interview. Discover why pre-training limits are here, what's next for AI research, and SSI's mission for safe superintelligence.

xAI launches Grok 4.1 with 2M context window, 3x lower hallucination rate, EQ-Bench3 #1 ranking, and ultra-affordable API pricing at $0.20 input/$0.50 output per 1M tokens. Full performance breakdown & pricing guide.

Google Gemini 3 Pro tops LMSYS Arena with record 1501 Elo score and dominates GPT-5.1 on AGI-critical benchmarks including Humanity's Last Exam (37.5% vs 26.5%) and ARC-AGI (45.1%), while achieving 100% on AIME 2025 with code execution.

Explore Google's Nano Banana Pro (Gemini 3 Pro Image), the new AI image generation model with perfect text rendering and character consistency. Find out where to use it.

Calculate token costs for different LLM architectures, compare model efficiency, and optimize inference budgets

Comprehensive guide to Large Language Models: how LLMs work, Transformer architecture, training process, prompt engineering, and real-world applications. Learn about GPT, Claude, Gemini, and more.

Complete guide to Transformer architecture: self-attention mechanisms, encoder-decoder design, and how Transformers power GPT, BERT, and modern LLMs. With code examples and visual diagrams.

Compare 7 LLM sampling methods: Top-P (Nucleus), Temperature, Beam Search, Min-P, Mirostat. Fix repetitive outputs, improve quality. Includes parameter tuning guide for GPT/Claude/Gemini.

Compare DeepSeek V3 vs Llama 4 architecture: MLA vs GQA attention, MoE vs dense models. Learn how 671B parameters run at 37B speed. Includes code examples and design trade-offs.

DeepSeek V3 Multi-head Latent Attention (MLA) cuts KV cache 4-8x vs standard MHA. Learn low-rank compression, matrix absorption, prefill vs decode phases. Complete PyTorch implementation with tensor shapes.

Discover how linear layers enable multi-head attention in Transformers, powering advanced NLP models with parallel processing and rich representations.

Learn how to train a language model with this PyTorch training loop guide. Explore text generation, the AdamW optimizer, and Mixture of Experts models.

Learn how to add special tokens to LLMs during fine-tuning without causing catastrophic forgetting. Our guide covers smart initialization and PEFT/LoRA.

Fix Qwen3 FP16 overflow on mobile devices: QK-Norm explained with code examples. Deploy LLMs on edge hardware (RTX 3060, mobile chips) with 90% error reduction.

Compress LLMs 10-100x smaller using knowledge distillation. Learn teacher-student training, temperature scaling (T=3-5), soft targets. DistilBERT case: 40% smaller, 60% faster, 97% accuracy. Complete tutorial.

Learn how to build a Llama-style MoE language model from scratch. This guide covers the Mixture of Experts architecture, tokenization, and model setup.

Explore the architecture of Alibaba's Qwen3-Next, a powerful large language model. Learn about its Mixture of Experts (MoE) design and performance.

The foundational Transformer paper introducing scaled dot-product attention and multi-head mechanisms.

Anthropic’s deep-dive into how Transformer attention heads implement reasoning and steering behaviour.

Interactive visual exploration of residual streams, attention patterns, and internal representations in GPT models.