#llama.cpp milestone
20 related articles
·2 分钟
llama.cpp Milestone: The Open-Source Evolution of Local LLM Inference
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.
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·3 分钟
The Open-Weights Model Debate: Balancing Safety and Openness
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.
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·3 分钟
The Open-Weights Model Debate: Balancing Safety and Openness
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.
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·3 分钟
Kimi K3 Local Deployment Guide: Storage Requirements for q4 and q8 Quantization Explained
Detailed analysis of Kimi K3 quantization deployment options, comparing q4 vs q8 storage requirements, precision trade-offs, and hardware configurations for local self-hosting.
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·7 分钟
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.
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AIRI: The Open-Source Self-Hosted AI C…
·2 分钟
AIRI: The Open-Source Self-Hosted AI Companion to Build Your Own Cyber Neuro-sama
AIRI (moeru-ai/airi) is an open-source, self-hosted AI companion with real-time voice chat, Minecraft/Factorio gameplay, and Web/macOS/Windows support. 42K+ GitHub stars.
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·2 分钟
Apple in Talks to Acquire PrismML: Can Extreme Quantization Really Run AI on an iPhone?
Apple is reportedly in talks to acquire AI startup PrismML, whose 1-bit extreme quantization could run large models on iPhone. Community tests reveal tool-calling failures and high hallucination rates.
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·4 分钟
INT4 ConvRot W4A4 Quantization: Running Large Image Models on Low-VRAM GPUs in ComfyUI
An in-depth look at INT4 ConvRot W4A4 quantization, covering conversions of Krea2, Qwen-Image, and other diffusion models to help ComfyUI users run large image models on 8GB GPUs.
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·5 分钟
28 Enterprise-Grade AI Agent Projects: A Complete Guide That Even Beginners Can Deploy
A collection of 28 fully reproducible enterprise-grade AI Agent projects covering code debugging, financial analysis, customer service, and multi-agent collaboration—deployable even for beginners.
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·6 分钟
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.
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·3 分钟
Unsloth Major Update: DiffusionGemma & Gemma 4 MTP Support, 2x Inference Speed Boost
Unsloth v0.1.464-beta adds DiffusionGemma, Gemma 4 MTP, and MiniMax-M3 support, delivering ~2x inference speed boost, new Hub, RAG Q&A, tensor parallelism, and full CUDA/ROCm/Windows coverage.
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·3 分钟
Running Local LLMs on AMD GPUs: Ditching Ollama for LM Studio Delivers 7x Speed Boost
AMD GPU black screens running local LLMs? This post-mortem covers Ollama's 3 fatal flaws and how switching to LM Studio boosted token speed from 5 to 36, with ROCm setup, Speculative Decoding, and GFX version tips.
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·4 分钟
The Complete Guide to Running GLM-5.2 Locally with 4 RTX 3090s for $4,000
How to build a local AI inference server with 4 used RTX 3090 SXM4 GPUs to run GLM-5.2 via Llama.cpp and Unsloth IQ quantization, with real benchmarks on speed and quality.
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·2 分钟
Open-Source Models in Real-World Android Development Testing: 50% Task Completion Rate Matches Previous-Gen Proprietary Models
Google Android Bench shows frontier open-source models solve 50-60% of Android dev tasks. Mid-size models like Gemma 4 run locally with just 20GB RAM.
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·1 分钟
Gemma 4 12B: Google's Open-Weight Model Runs Locally on Your Laptop
Google releases Gemma 4 12B, an open-weight model that runs locally on laptops. Learn about its performance, local deployment value, and the open-source LLM competitive landscape.
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产品体验·3 分钟
Google Gemma 4 Hands-On Review: Offline on Smartphones + Ollama Deployment Tutorial
Hands-on testing of Google Gemma 4 open-source models running offline on three phones, with Dense vs MOE architecture explained and a complete Ollama + Claude Code deployment tutorial.
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教程攻略·3 分钟
llama.cpp MTP Acceleration Deployment Guide: Configuration Steps & Real-World Benchmarks
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.
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教程攻略·3 分钟
oMLX + MTP + Qwen3.6: Local AI Coding Speed Breaks New Records
Using oMLX with MTP and Qwen3.6 35B on Apple Silicon Mac to achieve 86.7 tokens/s local coding speed, building a full-stack app in under 5 minutes.
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科技前沿·4 分钟
Step 3.5 Flash Deep Dive: How a 196B Parameter Open-Source Model Beat Gemini
Deep dive into StepFun's Step 3.5 Flash: 196B parameter MoE model activating only 11B, 350 tokens/sec coding speed, 256K context window, local deployment ready. How it beats Gemini 3 Flash.
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产品体验·3 分钟
Gemma 4 Deep Dive: Running Google's Top-Tier AI Free and Offline
Deep dive into Google's Gemma 4 open-source AI: local deployment tutorial, head-to-head comparison with ChatGPT, and offline phone demo. Four model sizes from mobile to workstation, zero-code setup via LM Studio, fully private and forever free.
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