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A practical guide to MoE post-training, covering the tradeoff between load balancing and task quality, why RL becomes unstable when routing changes across engines or policy versions, and how to choose EP versus ETP in large-scale deployments.

Master Google Nano Banana Pro to create publication-ready academic diagrams. Learn our proven 2-step workflow combining LLMs with AI image generation for CVPR/NeurIPS-quality scientific illustrations.

Discover how AI inference engines evolved from edge-optimized CNNs to cloud-scale LLMs. Learn the key differences between vLLM, TensorRT-LLM, and traditional frameworks like MNN and TVM in this comprehensive 2025 guide.

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.

Step-by-step TensorRT-LLM tutorial: Deploy Llama 3/GPT models 3x faster on A100/H100. Includes Python setup, Docker configuration, KV Cache optimization, and benchmarks vs vLLM. Complete guide in 20 minutes.

Boost RAG accuracy with chunking: 9 core strategies + 2026 upgrades (contextual retrieval, late chunking, cross-granularity). Practical defaults: 256-512 tokens, 10-20% overlap. Includes Python/LangChain code and production tips.

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

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

Learn the complete Supervised Fine-Tuning (SFT) pipeline to enhance LLM reasoning. This guide covers the DeepSeek R1 process, from SFT to knowledge distillation.

Learn to replicate the DeepSeek R1 training process. This guide covers building a reinforcement learning pipeline from scratch using GRPO for advanced LLM reasoning.

Fine-tune LLaMA 3 with zero coding in 3 steps using LLaMA Factory WebUI. Save 80% GPU memory with QLoRA on RTX 3090/4090. Beginner-friendly tutorial with CUDA setup. Supports 100+ models.

What is a transformer model in AI? Learn the Transformer architecture, self-attention, encoder-decoder flow, and how Transformers power GPT, BERT, Claude, and modern LLMs with diagrams and examples.

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.

Explore LLM inference optimization on H800 SuperPods. Learn how a disaggregated architecture with SGLang tackles the prefill bottleneck to boost throughput.

Master GPU performance optimization: Matrix multiplication achieves 90%+ FLOPS on A100, while CNNs get only 20% due to memory bandwidth bottleneck. Learn compute-bound vs memory-bound operations, fused kernels, Tensor Cores, and H100 FP8 improvements.

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.

Master Supervised Fine-Tuning (SFT) transforming base models to chat assistants. Complete 3-stage pipeline: base → instruct → chat model. LoRA reduces cost 70%, 7B model SFT in 2-4 hours on A100 ($10-20). Alpaca vs Dolly vs Open-Orca datasets compared.

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

Master training 671B parameter LLMs with RL. Solve 5 critical challenges: Megatron vs FSDP, memory offloading, weight conversion, 1000+ GPU scaling. Real DeepSeek V3 workflow with GRPO achieving 10x speedup.

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.