Latest Posts

By Wei Ming T. on May 25, 2025

Review on which LLMs actually perform best for coding tasks like UI/UX design, problem-solving, and refactoring, based not just benchmarks.

By Jacob M. on May 24, 2025

Choosing between PyTorch and TensorFlow? This guide details 5 differences covering API design, graph execution, deployment, and community, helping ML engineers select the optimal framework for their projects.

By Ryan A. on May 24, 2025

Understand the GGUF file format, its architecture, benefits for LLM inferencing, and its role in local model deployment. This guide offers technical professionals essential knowledge for creating, quantizing, and utilizing GGUF files effectively.

By Andreas T. on May 23, 2025

Discover 5 Proximal Policy Optimization (PPO) variants designed to elevate your Reinforcement Learning from Human Feedback (RLHF) pipelines. This technical guide explains how these modifications address common PPO limitations, leading to better LLM alignment and performance.

By Sam G. on May 22, 2025

Selecting the right database is fundamental for building high-performing RAG applications. This guide explores essential criteria, compares database types (vector-native vs. extended traditional DBs), and provides insights to help developers and ML engineers choose the optimal solution for vector search, scalability, and low-latency retrieval.

By Lea M. on May 20, 2025

Understand how effective chunking transforms RAG system performance. Explore various strategies, from fixed-size to semantic chunking, with practical code examples to help you choose the best approach for your LLM applications and improve context retrieval.

By Jack N. on May 14, 2025

Learn to dramatically reduce memory usage and accelerate your Large Language Models using bitsandbytes. This guide offers engineers step-by-step instructions and code examples for effective 4-bit and 8-bit LLM quantization, enhancing model deployment and fine-tuning capabilities.

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.