In-depth analysis of AI architectures, deployment patterns, and the research shaping the field.
Master LLM inference optimization: speculative decoding, KV-cache compression, quantization, FlashAttention, and serving frameworks compared for fast, cost-effective AI.
Explore how reasoning models like o1, o3, and DeepSeek-R1 use inference-time compute scaling and chain-of-thought to solve problems standard LLMs cannot.
Master the transformer architecture from first principles: self-attention, multi-head attention, positional encodings, encoder-decoder design, and modern innovations like RoPE, GQA, and SwiGLU, with code.
Learn how Mixture of Experts (MoE) powers frontier AI models like DeepSeek-V3 and Mixtral: sparse routing, load balancing, and why MoE beat dense scaling.