#LLM sparsity

24 related articles

Jun 23, 2026·3 min

Making LLMs Faster and Lighter: A Practical Approach to Reshaping Sparsity for GPUs

Deep dive into Sakana AI and NVIDIA's latest research using TwELL sparse packing format and custom CUDA kernels to convert LLM sparsity into real GPU speedups, achieving 20%+ faster inference/training and significantly lower memory usage.

Aug 5, 2026·4 min

Running an 80B Large Model on Mac with Only 4.3GB of Memory: Extreme Compression Techniques Explained

Deep dive into how an 80B-parameter LLM runs on Mac with only 4.3GB memory, covering ultra-low-bit quantization, sparsity, memory mapping, and implications for privacy and edge AI.

Aug 2, 2026·3 min

The Fundamental Conflict Between Backpropagation and Continual Learning: Can Catastrophic Forgetting Be Solved?

Exploring why standard backpropagation causes catastrophic forgetting, its fundamental conflict with continual learning, and whether solutions like EWC and experience replay can bridge the gap.

Jul 28, 2026·3 min

Kimi K3 Deep Dive: How a 3 Trillion Parameter Open-Weight Model Reaches the Frontier

Deep dive into Kimi K3: the largest open-weight model at 3 trillion parameters, surpassing Opus-level models in Agentic coding with 896-expert MoE architecture, 1M token context, at Sonnet pricing.

Jul 28, 2026·3 min

Deep Dive into Inkling: The First Frontier Open-Source Multimodal LLM from the West

Thinking Machines releases Inkling, an open-source multimodal LLM with near-trillion MoE parameters, 1M token context, Apache 2.0 license. Deep dive into architecture, benchmarks, and pricing.

Jul 27, 2026·7 min

DIY Companion Robot: Building Your AI Desktop Buddy with Jetson Orin

A maker builds a DIY companion robot with NVIDIA Jetson Orin and 4S LiPo battery. Explore the full development journey from first power-up to AI interaction, including edge computing, power design, and companion robot trends.

Jul 20, 2026·4 min

Understanding Transformer From the Ground Up: How Language Models Predict the Next Word

From Tokenization and Embedding to the Attention mechanism, this article systematically breaks down how Transformer works — and how ChatGPT turns input text into next-token probabilities.

Block Low-Rank Compression: A Guide to…

Jul 19, 2026·2 min

Block Low-Rank Compression: A Guide to Accelerating Large Model Inference on Memory-Constrained GPUs

Learn how Block Low-Rank (BLR) decomposition compresses large model memory usage and accelerates GPU inference, including CUDA kernel optimization and combination with quantization and pruning.

Jul 17, 2026·2 min

Training a CPU-Native LLM on Zero Budget: The Architectural Gamble of Ternary Quantization × Sparse MoE

An indie dev attempts to train a CPU-native LLM on $0 budget using ternary quantization, sparsity, and fine-grained MoE — with pre-registered success criteria and full public reporting.

Hardware-Software Co-Design: A Guide t…

Jul 15, 2026·2 min

Hardware-Software Co-Design: A Guide to Building Hardware-Friendly LLM Architectures

Explore AI Model Co-Design principles and how hardware-friendly LLM architecture design — covering MoE, GQA, and FP8 quantization — optimizes the accuracy, throughput, and latency trade-off.

Flash-MSA: How Sparse Attention Kernel…

Jul 14, 2026·2 min

Flash-MSA: How Sparse Attention Kernels Break the Million-Token Training Barrier

Flash-MSA uses GPU sparse attention kernels to reduce complexity from O(n²) to near-linear, tackling the compute bottleneck of million-token LLM training.

Jul 14, 2026·3 min

How to Evaluate AI Agents in Production: Four Online Evaluation Methods Explained

Offline tests passing doesn't mean production reliability. This guide breaks down Online Evals for AI Agents: rule checks, LLM-as-a-Judge, user feedback, and human review.

Jul 12, 2026·4 min

Unsloth Releases Qwen3.6 NVFP4 Quantization: 2.5x Faster with Zero Accuracy Loss

Unsloth releases NVFP4 quantization for Qwen3.6 using W4A4 true 4-bit Tensor Core computation, delivering up to 2.5x inference speedup over NVIDIA's official implementation with accuracy matching or exceeding BF16 on benchmarks like MMLU-Pro.

Jul 12, 2026·7 min

AI Agent Runs a Fully Automated Chess Channel: A Real-World Practice of Combining LLMs with Engines

A German engineer built a fully automated chess YouTube channel with an AI Agent, combining LLMs and chess engines to auto-generate explainer videos nightly, reaching 500K views. Here's the tech architecture, tool design, and real costs.

Jul 11, 2026·6 min

Is AI Dream Interpretation Really Accurate? Unpacking the Technical Limits and the Truth Behind Psychological Suggestion

AI dream interpretation and personality analysis are trending on social media, but can AI really understand you? This article unpacks the technical limits and hidden risks—from the Barnum Effect to LLMs.

Jul 9, 2026·6 min

AMD Ryzen AI Halo Dev Kit: A $4,000 Local LLM Powerhouse Taking on Apple and NVIDIA

AMD officially unveils the Ryzen AI Halo local AI dev kit, priced around $4,000 with 128GB unified memory, capable of running 70B LLMs locally. An in-depth look at its specs, pricing, and market competition.

Jul 7, 2026·4 min

Ternlight: A 7MB Browser-Side Embedding Model for Server-Free Semantic Search

Ternlight is a 7MB WebAssembly-based browser-side text embedding model requiring no server or GPU. Explore its tech, use cases, and tradeoffs for private, offline semantic search.

Morph Reflexes: Building Real-Time Beh…

Jul 7, 2026·4 min

Morph Reflexes: Building Real-Time Behavioral Guardrails for AI Agents with Multi-Head Classifiers

Morph Reflexes is an open-source AI agent monitoring tool that uses multi-head classifiers for real-time trace classification — enabling safety guardrails, quality scoring, and training data filtering.

Jul 7, 2026·7 min

The Complete Guide to AI-Powered Customer Service: How AI Is Reshaping Enterprise Service Systems

AI customer service is a core tool for digital transformation. This guide covers its value, use cases, and implementation logic, including efficiency gains, cost reduction, and data-driven optimization.

Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained

Deep Dives

Jun 3, 2026·3 min

Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained

Complete guide to the three core LLM training stages: pre-training, supervised fine-tuning (SFT), and preference alignment (DPO/PPO), covering LoRA, distillation, quantization, and pruning.