AI Model2026
Runs a 35B-class sparse MoE LLM with SSD-streamed experts, 4-bit quantization, prerouter routing prediction and Recover-LoRA adapters to enable ~2.9–3 GiB active memory and interactive decoding (~15 tok/s) for on-device inference.
AI Model2026
Preview agentic language model for research and engineering workflows that turns research questions into executable, verifiable workflows via tool use and long-context reasoning; built on a 744B-parameter MoE (GLM-5.2) with MIT-licensed BF16 and FP8 checkpoints.
AI Model2026
Compresses a 27B-class multimodal model into end-to-end ternary weights to run 27B reasoning on-device: 5.9–8.6 GB deployed footprint, 262K-token context, ~98.2% of FP16 benchmark performance; ships MLX and GGUF packs and runs on Apple MLX and CUDA.
AI Model2026
Runs locally on constrained devices to turn text into guaranteed-parsable JSON tool calls, typed structured extractions, or sentence embeddings. Delivered as a single compact weights file (8–29 MB) with a laddered 2–20-layer design, low-bit quantisation and calibrated confidence scores for on-device apps.
AI Model2026
A 29B-parameter mixture-of-experts LLM for long-context reasoning and agent-style workflows, with native 256K context and Ascend NPU co-optimizations. Compatible with Transformers/vLLM ecosystems; suitable for long-horizon agents and domain fine-tuning.
AI Deploy2026
Evaluates multi-field JSON schemas in parallel to extract boolean or categorical field values from text, producing guaranteed-valid JSON and per-field calibrated confidences. Uses KV-cache broadcasting, sub-vocabulary logit slicing and token-tree disambiguation to cut latency (5.6x–7.0x on M4 Max) versus autoregressive decoding; requires Apple Silicon and MLX.
Describes DeepSeek-V4.1-Flash, a 552B-parameter multimodal Mixture-of-Experts LLM that supports up to 1M-token contexts and reduces KV cache footprint using Compressed Sparse Attention 2, FP4 KV caching, and SWA Bounded Replay to lower HBM/SSD and prefill costs.
AI Audio2026
On-device speech-to-text for short clips (up to 30s) in seven languages, yielding transcripts, word-level timestamps and per-frame speech embeddings in a single 16.9 MB model. Runs on the Needle CPU engine with 2–4 bit quantization, supports keyword biasing and returns empty transcripts for silence.
AI Model2026
Maps multimodal inputs (text + images) to structured decisions (yes/no, choice, or scored rubric) in a single forward pass and returns calibrated probabilities. 3.1B parameters, long context (32,768 tokens), optimized for low-latency edge inference; not a text-generation/chat model.
AI Model2026
Provides a locally deployable, FP4-quantized variant of Qwen3.8-Flash-Next for multimodal (text, image, video) text generation. Uses NVIDIA Model Optimizer with quantization-aware distillation (QAD), a ~96 GiB checkpoint, and is validated on vLLM for Blackwell and DGX Spark.
AI Model2026
Runs a pruned, NVFP4-quantized GLM-5.3-Flash variant tuned for Blackwell GPUs: 224 routed experts per layer and ~141 GiB of weights. Retains the multimodal vision tower, activates 18B params/token, supports vLLM and optional MTP speculative decoding; fits 2× DGX Spark or a ≥180 GB B200.
AI Model2026
Multimodal image/video-to-text model optimized for on-device inference: the 1B variant uses early visual-token compression and multiple quantization formats to improve capability-per-compute, and supports mobile platforms and common inference frameworks.