AI Model2026
Performs feed‑forward streaming 3D reconstruction from image sequences, combining coordinate grounding, dense geometric cues and trajectory memory to correct long‑range drift; uses paged KV‑cache attention for ~20 FPS inference at 518×378 and supports sequences >10,000 frames.
AI Model2026
Unified multimodal LLM for enterprise workflows: ingests video, audio, image and text to perform transcription, OCR, Q&A, summarization and long-context reasoning. Provides BF16/FP8/NVFP4 weights and integrations with vLLM, TensorRT-LLM and other runtimes.
AI Infra2026
Provides high-performance CUDA/CUTLASS kernels implementing Kimi Delta Attention (KDA), accelerating KDA prefill on SM90+ (Hopper) GPUs. Integrates as a drop-in backend for flash-linear-attention, supports native variable-length batching, and targets K=V=128; requires CUDA 12.9+/PyTorch 2.4+.
AI Model2026
A 284B-parameter Mixture-of-Experts LLM with only 13B activated parameters, designed for 1,000,000-token contexts. Uses hybrid compressed attention and mixed FP4/FP8 precision to reduce long-context KV-cache and per-token FLOPs; aimed at long-document QA, RAG pipelines, and local/high-capacity inference.
AI Dataset2026
Provides instruction-based (before, after) structured 3D latents (SLAT) with aligned RGB views and natural-language edit prompts for training and evaluating instruction-following 3D editing models. Covers part-level semantic edits across seven edit types (deletion, addition, modification, scale, material, color, global) and supplies shard-based NPZ assets and loader code.
AI Model2026
Unifies multimodal image understanding, text-to-image generation, and instruction-based editing in a single diffusion LLM using a Mixture-of-Experts backbone, SigLIP-VQ discrete tokenizer, and a distilled diffusion decoder enabling fast (8-step) decoding; full-generation needs ~47GB GPU RAM.
AI Model2026
A 14B dense tri‑mode language model that supports autoregressive, diffusion‑based parallel decoding, and self‑speculation—designed to increase token throughput and acceptance length; best suited for researchers and engineers exploring decode‑efficiency tradeoffs on NVIDIA hardware under the Nemotron Open Model License.
AI Model2026
A Qwen-3.6 27B model variant optimized for DFlash (speculative decoding) to reduce generation latency and increase throughput. Focuses on faster inference on serving stacks and is suitable for text-generation endpoints where lower latency and resource efficiency matter.
AI Model2026
High-resolution vision transformers pretrained on one billion human images for human-centric tasks such as pose estimation, body-part segmentation, surface-normal and pointmap prediction. Provides multiple backbone sizes and task-specific checkpoints; released under the Sapiens2 license.
AI Video2026
Performs task-aware generative video restoration and editing in latent video space — restoration, super-resolution, watermark and subtitle removal — adapting LTX‑2.3 with IC‑Edit/IC‑LoRA adapters to prioritize temporal consistency and occlusion-aware reconstruction.
AI Dataset2026
Provides 104.9M curated image–text pairs with precomputed embeddings, structured annotations and pre-encoded VAE latents for text-to-image pretraining and retrieval. Combines filtered web sources and synthetic samples with multi-model re-captioning, deduplication and safety filters; Apache-2.0.
AI Model2026
Converts latent representations into high-resolution images by using a conditional pixel-space diffusion decoder that merges decoding and upsampling into a single generative step. Released checkpoints are 4-step distilled (2k and 2kto4k variants) and pair with specific VAE/encoder weights; license restricts use to non-commercial research.