World Wire

A single 24GB card is the practical floor for serious local inference. It is enough for genuinely capable models, and small enough to sit on one GPU. An RTX 3090 or RTX 4090 both land in this tier. The card you own matters less than the models you pick for it. The old hobbyist move was to squeeze the biggest 70B quant onto the card. That advice is now outdated. The stronger 2026 strategy uses modern 20B–35B-class models that fit cleanly. These leave room for context, and still respond fast enough for coding, chat, and agents. This guide covers the models that actually fit, why each is worth running, and how 24GB gets spent. How 24GB of VRAM actually gets spent Three things consume memory during inference. Getting the split right decides whether a model fits. The first is model weights . Their size depends on parameter count and quantization. At Q4_K_M, a common home-inference default, each parameter costs roughly 0.58 bytes. A 32B model therefore needs about 18–20GB ...

On July 19, Alibaba’s Qwen team previewed Qwen3.8-Max-Preview , the next flagship in the Qwen family. The research team describes it as a 2.4 trillion-parameter model, ‘second only to Fable 5’ among the systems it benchmarked. The preview is live now. The benchmark table, model card, and license are not. The July 19th 2026 announcement landed during the World AI Conference (WAIC) in Shanghai. It also arrived two days after Moonshot AI released Kimi K3 , a 2.8 trillion-parameter open-weight model. The timing is the story as much as the model. This article separates what Alibaba confirmed from what it only claimed. Every performance figure below carries that caveat. What Qwen announced The Qwen account posted that Qwen3.8 is launching and going open-weight soon. It called the model ‘one of the most powerful available today, comparable to leading frontier systems. The preview build is real and purchasable. Access runs through Alibaba’s T...

Research agents already handle real knowledge work today. Teams delegate competitive mapping, due diligence, and literature review to them. However, most benchmarks test a single answer, not large evidence-backed collections. Perplexity targets that gap with a new open benchmark. Perplexity released WANDR (Wide ANd Deep Research) . It is an open benchmark and evaluation harness. It is built around 500 realistic, challenging data-collection tasks for knowledge work. WANDR is the wide sibling of Perplexity’s DRACO benchmark for deep research. DRACO asks whether an agent produces an accurate, complete, objective long-form report. WANDR instead asks whether it can build a large collection with evidence. What is WANDR At its core, WANDR tests two demands together. Wide means discovering a large, often open-ended set of qualifying entities. Deep means investigating every entity enough to support each claim with evidence. Combining both changes the problem for agents. A f...

Introduction Building an LLM application no longer requires wiring orchestration code by hand. A class of open-source platforms now exposes retrieval, agents, and workflows through visual canvases, web UIs, and plain-English prompts. These tools let developers prototype in minutes and self-host for data control. This article reviews ten open-source projects across three jobs: building LLM apps, building RAG systems, and building AI agents. Each entry covers what the tool does, its core capabilities, who it suits, and its verified license and repository. HKUDS AutoAgent Repository: github.com/HKUDS/AutoAgent · License: MIT · Paper: arXiv:2502.05957 AutoAgent is a zero-code agent framework from the University of Hong Kong Data Intelligence Lab . You describe a goal in natural language. The system then constructs tools, agents, and multi-agent workflows without manual coding. It ships an agent editor, a workflow editor, and a ready-to-use research...