189 related articles

Quantprobe is an open-source memory optimization framework that enables 30B LLMs to run at 22 tokens/s on 6GB GPUs through per-layer quantization and intelligent CPU/GPU splitting.

Alibaba's Qwen3.8-Max-Preview iterates daily with significant frontend development improvements. The team uses an open preview strategy to collect community feedback, promising open-weight release.

An insider's analysis of China's four AI labs — Qwen, DeepSeek, Moonshot, and Ling — revealing their distinct strategic bets on distribution, architecture, long-termism, and serving cost.

Deep analysis of Nightcrawler, an AI penetration testing agent running entirely on smartphones. Exploring how on-device AI empowers cybersecurity testing, its architecture, use cases, and risks.

Redis creator antirez open-sources ds4, a pure C local inference engine for DeepSeek 4 Flash and PRO with native Metal, CUDA, and ROCm support, earning nearly 20K GitHub stars.

Deep dive into how Tokens evolved from a technical concept in LLMs to the core unit of measurement in the AI economy. Exploring Token consumption explosion, cost optimization, and Token economics.

In-depth analysis of MiniMax H3 local video generation capabilities, exploring hardware requirements, advantages, challenges, and the trend of AI video moving from cloud to local deployment.

Exploring how persistent state machines with INT4-quantized memory cells reshape LLM attention, breaking KV Cache memory bottlenecks for long-context inference on edge devices and high-concurrency scenarios.

Benchmarking DeepSeek V4 Flash on dual RTX 3060 GPUs with 96GB RAM at IQ2_M quantization achieving 3.5 tokens/sec. Covers hardware choices, 2-bit quantization techniques, and local LLM deployment optimization.

24GB Mac Mini too slow for local LLMs? Learn why 14B models struggle, get 3B-8B model recommendations for Home Assistant, and discover Ollama speed optimization tips.

Learn how to complete LLM post-training on a consumer GPU with just 8GB VRAM, covering SFT, DPO, and GRPO methods using LoRA quantization and other techniques.

DeepSeek-V4-Flash-0731 scores 50 on the Intelligence Index, nearly matching the frontier model score of 51 from five months prior. We analyze local deployment, hardware requirements, and implications.

Deep analysis of the real cost of serving a 2.8 trillion parameter model. From MoE sparse activation to batching scale effects and inference optimization, revealing why model size and serving cost are less correlated than assumed.

Complete guide to setting up a local AI coding environment on MacBook Pro M4, covering Ollama, MLX, Continue, Qwen3-Coder 30B configuration, and performance optimization strategies for 32GB RAM.

GPT-5.6 Luna tops Google's flagship on the Artificial Analysis Intelligence Index while priced below Google's entry-level model. A deep dive into what this performance-cost breakthrough means.

DeepSeek V4 Flash model weights reportedly open-sourced. This article analyzes its lightweight positioning, open-weight value, comparisons with closed-source models, and deployment guidance.

Running Kimi K3 with 29GB RAM at just 0.5 tok/s. An in-depth analysis of extreme quantization techniques, performance trade-offs, and the impossible triangle of local LLM deployment.

Running Kimi K3 with 29GB RAM at just 0.5 tok/s. A deep analysis of extreme quantization techniques, performance trade-offs, and the impossible triangle of local LLM deployment.

In-depth analysis of DeepSeek-V4-Flash model's positioning and technical path. Exploring the lightweight trend behind the Flash naming, MLA attention, MoE architecture, and its significance for open-source AI.

In-depth analysis of DeepSeek-V4-Flash model's product positioning and technical approach. Examining lightweight trends through the Flash naming, MLA attention mechanism, MoE architecture evolution, and implications for the open-source AI ecosystem.