World Wire

NVIDIA has just released Nemotron 3.5 Bolt E4B 4GB , an open small language model for fast conversational agents. It targets voice assistants, game NPCs and robot interfaces running locally on RTX, Jetson and Spark hardware. The model is post-trained from Google’s Gemma 4 E4B using reinforcement learning only. For developers, this is agent behavior in about 4GB of VRAM. That leaves room for speech and vision models. TL;DR Size: 8B total parameters , 4B effective, 64K context. Runs on: GeForce RTX, Jetson, RTX Spark and DGX Spark. The Q4_K_M build used 3.1 to 4.3 GB VRAM on RTX in NVIDIA tests. Performance: Top score on 15 of 21 benchmarks against Gemma 4 E4B, Qwen3.5-4B and Nemotron 3 Nano 4B (NVIDIA-measured). Best: IHEval 84.8, versus 67.0 for Gemma 4 E4B and 60.6 for Qwen3.5-4B. Worst: Tau2-Bench 55.1, about 20 points behind Qwen3.5-4B at 75.1. What is Nemotron 3.5 Bolt E4B? Nemotron 3.5 Bolt E4B is a small language model from NVIDIA tha...

Kyutai has released CADRE , open causal diffusion representation autoencoders for interactive world models. The Kyutai CADRE video autoencoder targets one problem: action lag. Many world models borrow autoencoders from text-to-video generators. Those autoencoders pack many frames into one latent, so every action waits. Per the research paper , CADRE world models run at 20 fps on 1 H100 and react within 230 ms. TL;DR Size: 7 autoencoders, each about 476M trained parameters plus a frozen 86M DINOv3 ViT-B/16 . The world model is 1.2B parameters. Video runs at 288×512, 20 fps. Runs on: 1 H100 (bf16, batch 1). Quantized variants: Not disclosed. Performance: CADRE forms most of the latency-quality frontier. It is as fast and better in 42 of 48 comparisons with MIRA, Wan 2.2, Cosmos and LTX-2. Best: Driving FVD of 162.6 (CADRE-2×16×16, 1 step) against 599.7 for Wan 2.2. Worst: Robot manipulation action following (ARR) of 0.45 for CADRE-2×32×32, against 0.85 for ...

Moonshot AI has just released Kimi K3.1 Pro , the first model in its K3.1 series. Kimi K3.1 Pro is a 3T-class open-weight* model with a 1-million-token context window. Moonshot team positions it against the strongest proprietary models on coding, agentic and knowledge benchmarks. For developers, it is cheaper API pricing than K3 and open weights yet not available. *open weights are expected to release in November 2026 . TL;DR Size: 2.8T total, 104B active parameters (MoE) , 1M-token context. Runs on: Kimi API today. For K3, Moonshot recommends supernodes of 64+ accelerators , with MXFP4 weights and MXFP8 activations. Not a single-GPU model. Performance: Near the proprietary frontier on terminal coding; behind it on scientific terminal tasks. Best: SWE-Marathon 51.7, ahead of every model in Kimi’s chart (self-reported). Worst: Terminal-Bench Science 0.1 at 38.1, versus 68.1 for GPT-6 Astra in Kimi’s chart. What is Kimi K3.1 Pro? Kimi K3...