🚀 RELEASES & LAUNCHES
GrootTeam releases Groot-280B-A16B open model on Hugging Face under MIT license
GrootTeam released Groot-280B-A16B-261023, an open-weights image-text-to-text model on Hugging Face, alongside a smaller 19B variant.- For local-model enthusiasts and developers running open LLMs via vLLM, SGLang, Transformers, or Docker.
- Groot-280B-A16B-261023 is a 280B-parameter MoE model (16B active) with image-text-to-text pipeline, MIT license, 8-bit/fp8 precision, and deepseek_v41 architecture.
- A smaller Groot-19B-A3B-261023 variant is also out; the poster claims it scores higher than Qwen3.6-35B-A3B and Gemma-4-26B-A4B with shorter answers.
- The Reddit poster suggests it may be Qwen-based with ngram additions and asks whether it's the best model under 500B params.
- It matters because a large open-weights MoE under MIT could shift local/self-hosted model choices if benchmarks hold up.
NVIDIA releases Nemotron 3.5 Bolt E4B 4GB, an open small model for local conversational agents
NVIDIA released Nemotron 3.5 Bolt E4B 4GB, an open small language model post-trained from Gemma 4 E4B for local conversational and agentic use.- Aimed at developers of voice/device assistants, interactive characters, and robotic/IoT interfaces running on GeForce RTX, Jetson, RTX Spark and DGX Spark.
- Built on Gemma 4 E4B (8B parameters, 4B effective) with Nemotron post-training; fits in ~4GB, offers 64K context, 35-language support, multi-turn tool use, conversational mode, and configurable reasoning.
- Reports ~40% higher average quality than NVIDIA's prior Nano 4B on conversational agent benchmarks and up to ~80% fewer output tokens vs peers; scored 53.1 on MultiChallenge vs Gemma 4 E4B's 42.1.
- Open weights, datasets and recipes are available on Hugging Face; demoed powering a Reachy Mini robot on Jetson Orin Nano 8GB with Cosmos handling vision.
- Matters because it packs conversational agent intelligence into ~4GB, enabling responsive local experiences while leaving memory headroom for other models in the pipeline.
🔬 RESEARCH
- r/LocalLLaMA user measures how inference engines' speed flags degrade model quality
A developer released Basalt and experiments showing LLM inference engines' lossy speed optimizations measurably degrade model output quality.- For local LLM users running quantized models on consumer engines, who suspect 'lobotomization' from lossy engine flags.
- A Redditor built an experiment using Qwen 3.8 Flash Next with their tool Basalt, testing KLD and real tasks.
- Two common lossy flags were tested: routing bias (steering toward experts already on GPU) and tail skip (dropping offloaded expert picks), both faster because fewer experts load from RAM.
- Matters because it shows engines can silently trade quality for decode/prefill speed, giving builders a way to measure the effect.
- Context: existing benchmarks like MLPerf Inference, Bench360, and llm-ie-benchmarks focus on throughput, not output quality degradation.
💰 BUSINESS & FUNDING
- OpenAI reportedly cuts funding target to $20B amid tepid investor response
OpenAI has lowered its new funding round target from $30 billion to $20 billion, with a valuation cut from $1.4T to $1.25T.- OpenAI's new funding round, previously reported at $30 billion, has been cut to a $20 billion target per the Financial Times, with a first close expected even lower.
- The valuation target was reduced from $1.4 trillion to $1.25 trillion after investor pushback; the WSJ reports only $11 billion secured so far amid a "tepid" response from large investors.
- The round will be led by a group of UAE investors, with a possible subsequent retail offering for wealthy individuals to reach the $20 billion target.
- The round precedes a planned IPO; challenges may reflect Wall Street wariness of heavy AI infrastructure spending and a major tech stock selloff.
- Microsoft, OpenAI's largest listed backer with roughly a 27% stake and a $250B Azure contract through 2032, saw shares fall 2.23% on the valuation cut news.
Sources 📎 Coverage: techcrunch.com · 👁 via TechCrunch AI