202 related articles

jlens-gguf is an open-source tool bringing Anthropic's Jacobian Lens interpretability method to GGUF and llama.cpp, enabling internal observation, real-time steering, and abliteration for both dense and MoE models.

llama.cpp hits a new milestone, growing from a solo hobby project into core local AI inference infrastructure. Explore its iteration speed, GGUF quantization, and how AI coding agents are reshaping open-source development.

Step-by-step guide to deploying Llama.cpp on Windows without compiling. Download pre-built packages, configure CUDA, and run GGUF quantized models locally with GPU acceleration and web UI in three simple steps.
教程攻略Guide to enabling MTP multi-Token prediction acceleration in llama.cpp, covering CUDA setup, desktop configuration, model selection, and benchmarks showing ~60 Token/s with Qwen3 27B.

Cursor AI coding tool accused of uploading user code to servers even with telemetry disabled. Analyze the controversy, privacy mode details, and security recommendations for enterprise developers.

A deep dive into HuggingFace's speech-to-speech open-source project, covering its modular VAD, STT, LLM, and TTS pipeline architecture and the advantages of local deployment for privacy, cost, and latency.

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Colibri uses MoE hot-cold separation and 4-bit quantization to run 744B-parameter models like GLM 5.2 on consumer hardware. Learn about its three-tier memory architecture and speculative decoding.

In-depth analysis of Apple Silicon local LLM inference speed benchmarks covering M-series memory bandwidth, model quantization, MLX framework optimization, and Mac configuration guidance.

An in-depth analysis of the open-weights model debate: public release brings transparency and innovation, but raises safety and misuse risks. Exploring tiered release, red-teaming, and governance challenges.

An in-depth analysis of the open-weights model debate: publicly releasing model weights enables transparency and innovation but raises safety risks. Explores tiered release, red-teaming, and the industry dynamics behind open AI governance.

Kimi K3 officially launches on Ollama Cloud as an "extra high usage" model. This guide covers free tier quotas, cloud inference experience, technical advantages, and how developers can seamlessly call this high-performance LLM.

Ollama lists Kimi 3 with extra pay-per-use fees, breaking subscription expectations and sparking debate over open-weight models and AI service pricing tiers.

RX 9060 XT vs RTX 5060 Ti — both 16GB VRAM, but which is better for local AI? We compare CUDA ecosystem, ROCm compatibility, LLM inference, and real-world usability.

RX 9060 XT vs RTX 5060 Ti both offer 16GB VRAM — which is better for local AI inference? A full comparison of CUDA ecosystem, ROCm compatibility, LLM performance, and real-world usability.

Anthropic has never open-sourced Claude's model weights. As OpenAI, Meta, and Google embrace open source, is Anthropic's AI safety stance genuine caution or a commercial moat? A deep dive into the debate.

Detailed analysis of Kimi K3 quantization deployment options, comparing q4 vs q8 storage requirements, precision trade-offs, and hardware configurations for local self-hosting.

HuggingHack releases major updates with S3/MinIO storage, Ollama + vLLM dual-engine scheduling, GGUF inspection, and local accounts for enterprise-grade local LLM management.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.