Mixture of Experts

An in-depth technical analysis of DeepSeek's Engram architecture, exploring how a modernized N-gram lookup module improves transformer reasoning, code generation, and long-context retrieval.

An in-depth technical review of Meta's Muse Spark Open, an open-weight frontier multimodal mixture-of-experts model, covering architecture, performance, and API implementation.

An in-depth review of AWS kernel optimizations for fused Mixture-of-Experts models on Amazon SageMaker AI, detailing Swap-AB, Triton FP8 routing, and elimination of JIT recompilation.

Learn how VIDRAFT's POCKET-Darwin-180B model uses graft quantization and SSD offloading in llama.cpp to run a top-ranking 180-billion-parameter MoE model on consumer laptops and mini PCs.

An in-depth analysis of the Kimi k3 technical report, revealing how a 2.8-trillion-parameter model is constructed through massive data engineering and specialized infrastructure rather than just architectural innovation.

An in-depth analysis of Mistral AI's strategic positioning in the global LLM market, examining how open-weight models are challenging proprietary giants and why timing is everything for the French AI lab.

A comprehensive guide to Mistral AI, the European startup challenging OpenAI with high-performance open-weight models like Mixtral and Mistral Large.

Move beyond basic API calls and learn the technical realities of deploying Google's Gemma 4 family on local HPC clusters and workstation hardware.

An in-depth technical analysis of Qwen3-Coder-Next, exploring how its sparse MoE and hybrid linear attention architecture achieve frontier-level coding performance with only 3B active parameters.

An in-depth technical analysis of the EMO framework, exploring how Mixture of Experts (MoE) models can achieve true modularity through specialized pretraining techniques and the implications for the future of efficient LLM scaling.

A comprehensive technical breakdown of DeepSeek V4 Pro, featuring its 1.6T MoE architecture, 1M context window, and the new dual-mode reasoning for agentic workflows.

A deep dive into Meta's first MoE-based Llama models, featuring 10M context windows, iRoPE architecture, and full deployment playbooks for vLLM and Ollama.