#model quantization
546 related articles
·6 min
AI Application Engineer Interview Guide: Core Topics in Model Quantization and Inference Deployment
A systematic review of must-know topics for AI Application Engineer interviews: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
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·6 min
AI Application Engineer Interview Guide: Core Topics on Model Quantization and Inference Deployment
A systematic guide to must-know AI application engineer interview topics: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
Read more →
·3 min
Running a 27B Model on iPhone: How 1-bit Quantization Achieves Extreme Compression
PrismML's Bonsai compresses a 27B model from 54GB to 3.9GB, running at ~11 tokens/sec on iPhone. A deep dive into QAT, knowledge distillation, and speculative decoding.
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·4 min
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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·4 min
NVFP4 Quantization in Practice: A Guide to Compressing Large Models with NVIDIA Model Optimizer
Deep dive into NVFP4 quantization: using NVIDIA Model Optimizer to compress Nemotron 3 Ultra to FP4 checkpoints, reducing memory by 75% and boosting inference throughput on Blackwell GPUs.
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·2 min
Chrome Built-in AI Explained: How Locally Running Models Protect Your Privacy
Explore Chrome Built-in AI technology and how running AI models locally in the browser enables zero data upload, instant responses, and stronger privacy protection.
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·2 min
Chrome Built-in AI Explained: How Locally Running Models Protect Your Privacy
Deep dive into Chrome Built-in AI technology, exploring how running AI models locally in the browser achieves zero data uploads, instant responses, and stronger privacy protection.
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·3 min
Apple Silicon LLM Inference Speed Benchmarks: A Complete Guide to Local Large Model Performance on Mac
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.
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·3 min
Monocular RGB Human Mesh Reconstruction: Real-Time Motion Capture at 18 FPS with Rust + CUDA
Deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on RTX 5080. Exploring its architecture, Metal porting plans, and applications in VTuber, AR/VR, and sports analysis.
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·3 min
Monocular RGB Human Mesh Reconstruction: Real-Time Motion Capture at 18 FPS with Rust + CUDA
A deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on an RTX 5080.
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·4 min
Getting Started with AI Agent Development: A Complete Guide from Concept to Practice
A comprehensive guide to AI Agent architecture and development, covering automated marketing, intelligent customer service, and investment analysis scenarios with single and multi-agent collaboration.
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·3 min
A Beginner's Guide to AI Agents: Two Learning Paths for Developers and Practitioners
How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.
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·3 min
AMD CDNA5 Architecture Deep Dive: Technical Evolution and the AI Computing Competition Landscape
Deep analysis of AMD's CDNA5 architecture covering Chiplet packaging upgrades, HBM memory evolution, and low-precision compute optimization, examining how AMD challenges NVIDIA's AI chip dominance.
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·4 min
A Beginner's Guide to AI Agents: Core Principles and Learning Paths Explained
Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.
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·3 min
From RAG to Agent Tuning: A Complete Guide to Deploying LLM Agents
A deep dive into LLM Agent frameworks covering RAG, Agent core components (tools, memory, planning), and Agent Tuning workflows with cost considerations for production deployment.
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·3 min
From Frontend to Agent Development: A Complete 6-Week Systematic Learning Path
A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.
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·3 min
Claude Opus 5 Officially Released: Performance Approaching the Top Tier as the Open-Weight Debate Heats Up Again
Anthropic releases Claude Opus 5 with near-frontier performance at lower prices. Same day, Jensen Huang co-signs open-weight letter with 20+ companies while DeepSeek fundraising rumors surface.
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·3 min
AI Weekly: Sandbox Escape Sparks Safety Legislation, Kimi Open-Source Triggers Geopolitical Dynamics
Deep analysis of five key AI events this week: OpenAI sandbox escape driving safety legislation, Kimi K3 open-source sparking geopolitical debate, Gemini Flash full rollout, Anthropic's $1.5B copyright settlement, and Chinese models' mobile expansion.
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·3 min
Fields Medal Winner Joins OpenAI: A Triple Signal of Talent, Capital, and Capability
Fields Medal winner Jacob Tsimerman joins OpenAI's safety team on award day, declaring math careers won't survive. Meanwhile, NVIDIA finances a $250B data center and Kimi K3 open-sources 2.8T parameters.
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·3 min
Fields Medal Winner Joins OpenAI: A Triple Signal of Talent, Capital, and Capability
Fields Medal winner Jacob Tsimerman joins OpenAI's safety team on award day, saying math careers won't survive. NVIDIA finances a $250B data center. Kimi K3 opens a 2.8T-parameter model.
Read more →