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Qwen 3.5's MoE models hit S-tier benchmarks, NVIDIA's Nemotron 3 Super delivers 5x throughput gains, and GLM-4.7-Flash brings frontier coding to consumer GPUs. The open-weight race just accelerated.
An open-source AI agent using interactive scaling beats OpenAI's GPT-5-high on Humanity's Last Exam. Here's what makes it different.
This week's open-source highlights: GPT-OSS marks OpenAI's first open weights since GPT-2, Superpowers becomes the most-starred AI coding framework, and Hunter Alpha was Xiaomi all along.
Jensen Huang bets on inference chips, Ollama adds multimodal support, and DeepSeek V4 remains the most anticipated release that hasn't happened yet.
Mistral drops a 119B MoE model under Apache 2.0, DeepSeek V4 emerges from stealth, and dual RTX 5090 setups are matching H100 on 70B inference. This week changed the game.
GTC 2026's biggest announcements were open-source. Nemotron 3 Super runs locally on RTX PCs, LTX 2.3 generates 4K video with audio, and vLLM hits production grade.
TikTok's parent company just open-sourced a powerful framework for running coordinated AI agents on your own hardware. Here's what it does and how to set it up.
Which local models can actually use tools, call functions, and run multi-step workflows? Function-calling and TAU-bench picks from 8GB to 32GB VRAM.
Head-to-head comparison of local chat and assistant models from 8GB to 32GB VRAM. Current picks: Qwen3.5, Gemma 4, GPT-OSS, Qwen3.6, and GLM-4.7-Flash.
Which open-weight coding model to run locally? HumanEval and SWE-bench picks from 8GB to 32GB GPUs, with IDE setup. Qwen2.5-Coder, Qwen3.6, Devstral.
Voice cloning, transcription, and TTS without the cloud. Parakeet, Canary, Whisper, Step-Audio-EditX, and Kokoro tested from 8GB to 32GB VRAM.
From TranslateGemma to LLM-based translation with Qwen and Aya Expanse. Privacy-first alternatives to Google Translate and DeepL, tested per GPU tier.
Run image analysis, document OCR, and visual reasoning locally. Qwen3-VL, InternVL3.5, Molmo2, and MiniCPM-V tested from 8GB to 32GB VRAM.
Georgi Gerganov's team is now at Hugging Face, unifying the model hub with the inference engine that powers Ollama, LM Studio, and the entire local AI ecosystem.
Complete guide to running local AI on 12GB GPUs - chat, coding, translation, vision, speech, and agents. The comfortable tier for RTX 3060 12GB and RTX 4070.
Complete guide to running local AI on 16GB GPUs - chat, coding, translation, vision, speech, and agents. The sweet spot for RTX 4060 Ti, RTX 5060, and Arc A770.
Complete guide to running local AI on 24GB GPUs - chat, coding, translation, vision, speech, and agents. Where local models start competing with cloud APIs. RTX 3090 and RTX 4090.
Complete guide to running local AI on 32GB GPUs - chat, coding, translation, vision, speech, and agents. The new frontier with RTX 5090. Near-lossless quantization and 70B models on a single card.
Run local AI on 8GB GPUs - chat, coding, vision, speech, and agents. Current model picks and honest limits for RTX 4060, RTX 3070, and similar cards.
Two weeks after our last roundup, the 5090 benchmarks are in and Qwen 3.5 Small models are running on phones. Here's the real performance picture.
The new MacBook Pro with M5 Max can run large language models entirely on-device, keeping your AI interactions private and offline
Alibaba's new 0.8B to 9B parameter models deliver GPT-class multimodal performance on consumer hardware, with the 9B variant outperforming models 13 times its size
This week's open-source highlights: AI2's hybrid architecture proves transformers need help, autoresearch automates ML experiments overnight, and local inference gets serious upgrades.
Nvidia open-sources a 30B-parameter reasoning model that runs on consumer GPUs with a million-token context window. Here's what makes it different.
