Latest Posts

By Wei Ming T. on May 1, 2025

Stop assuming MoE models automatically mean less VRAM or faster speed locally. Understand the real hardware needs and performance trade-offs for MoE LLMs.

By Wei Ming T. on Apr 23, 2025

Accurately estimate the VRAM needed to run or fine-tune Large Language Models. Avoid OOM errors and optimize resource allocation by understanding how model size, precision, batch size, sequence length, and optimization techniques impact GPU memory usage. Includes formulas, code examples, and practical tips.

By Jack N. on Apr 18, 2025

Learn 5 key LLM quantization techniques to reduce model size and improve inference speed without significant accuracy loss. Includes technical details and code snippets for engineers.

By Sam G. on Apr 18, 2025

Struggling with TensorFlow and NVIDIA GPU compatibility? This guide provides clear steps and tested configurations to help you select the correct TensorFlow, CUDA, and cuDNN versions for optimal performance and stability. Avoid common setup errors and ensure your ML environment is correctly configured.

By Ryan A. on Apr 18, 2025

Discover the optimal local Large Language Models (LLMs) to run on your NVIDIA RTX 40 series GPU. This guide provides recommendations tailored to each GPU's VRAM (from RTX 4060 to 4090), covering model selection, quantization techniques (GGUF, GPTQ), performance expectations, and essential tools like Ollama, Llama.cpp, and Hugging Face Transformers.

By Wei Ming T. on Apr 18, 2025

Learn the practical steps to build and train Mixture of Experts (MoE) models using PyTorch. This guide covers the MoE architecture, gating networks, expert modules, and essential training techniques like load balancing, complete with code examples for machine learning engineers.

By Stéphane A. on Apr 17, 2025

Understand the core differences between LIME and SHAP, two leading model explainability techniques. Learn how each method works, their respective strengths and weaknesses, and practical guidance on when to choose one over the other for interpreting your machine learning models.

By Sam G. on Apr 15, 2025

Transformer models can overfit quickly if not properly regularized. This post breaks down practical and effective regularization strategies used in modern transformer architectures, based on research and experience building large-scale models.

By George M. on Apr 15, 2025

Learn the most effective prompt engineering techniques recommended by Google. Includes actionable examples and clear dos and don’ts to improve your prompts.

By Wei Ming T. on Apr 9, 2025

Learn common causes and practical methods to debug and fix frustrating shape mismatch errors in PyTorch matrix multiplication and linear layers. Includes code examples and debugging tips.