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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.

Master distributed RLHF frameworks: Compare OpenRLHF and veRL architectures. Learn Ray Actors, GPU colocation, PPO implementation, and hybrid engine design for scalable reinforcement learning systems.

We tested 10 multi-agent AI frameworks and ranked them by use case. CrewAI wins for production, OpenAI Swarm for learning, AutoGen for complex tasks. Benchmarks, code examples, and decision matrix included.

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.

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.

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 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.

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.

Explore the shift to separated architectures for RL post-training of LLMs. Learn how systems like AsyncFlow & TransferQueue solve data orchestration challenges.

Monitoring **PyTorch GPU memory usage** during model training can be perplexing. To demystify this, we'll dive into the **PyTorch memory snapshot** tool, a powerful utility for detailed **GPU memory ...

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.

Explore single-controller vs multi-controller in veRL, inspired by Google's Pathways, and learn their impact on distributed reinforcement learning systems.

SGLang crushes vLLM with 3x throughput and 40% cost savings via prefill-decode separation. Real H800/A100 benchmarks, architecture deep-dive, production deployment guide. The future of LLM inference.

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.