Liquid AI

NVIDIA's RTX Spark laptops are now on sale from ASUS, Dell, HP, Lenovo, Microsoft and MSI starting at $2,599, pairing a Blackwell RTX GPU with a Grace CPU on one superchip with up to 128GB of unified memory and up to 1 petaflop of FP4 AI performance. The accompanying updates include Nemotron 3.5 Transcribe, an open weight speech model that handles transcription, speaker labeling and translation in one promptable model across 40 languages, ranking No. 1 on Hugging Face Open ASR with about 4.3 billion active parameters. A new NVIDIA App resource control lets users split GPU resources between foreground apps and background agents.

NVIDIA's RTX Spark laptops are now on sale from ASUS, Dell, HP, Lenovo, Microsoft and MSI starting at $2,599, pairing a Blackwell RTX GPU with a Grace CPU on one superchip with up to 128GB of unified memory and up to 1 petaflop of FP4 AI performance. The accompanying updates include Nemotron 3.5 Transcribe, an open weight speech model that handles transcription, speaker labeling and translation in one promptable model across 40 languages, ranking No. 1 on Hugging Face Open ASR with about 4.3 billion active parameters. A new NVIDIA App resource control lets users split GPU resources between foreground apps and background agents.

Liquid AI released LFM3, a family that puts text, vision, audio, omni and decision models on one shared backbone of roughly 2B parameters, with base and post-trained weights open-weight on Hugging Face and its API. The backbone mixes gated short convolutions, sliding-window attention and global attention, was pre-trained on 22T tokens, and a final 3.5T-token mid-training stage extends context to 256K. Per parameter, LFM3-1.7B matches LFM2-1.2B on short prompts across edge devices and is about 25% faster with 8K tokens in context.

Liquid AI released LFM3, a family that puts text, vision, audio, omni and decision models on one shared backbone of roughly 2B parameters, with base and post-trained weights open-weight on Hugging Face and its API. The backbone mixes gated short convolutions, sliding-window attention and global attention, was pre-trained on 22T tokens, and a final 3.5T-token mid-training stage extends context to 256K. Per parameter, LFM3-1.7B matches LFM2-1.2B on short prompts across edge devices and is about 25% faster with 8K tokens in context.

Liquid AI released d1-ultra, a multimodal decision model priced at $0.042 per million input tokens on the Liquid API. It targets harder, messier real-world cases such as spotting a defect on a circuit board against its spec or checking a damage claim against a customer photo, and answers three question types: noul, Choice, and Score. Compared with the previous d1 and Jev 1.14 across 112 unique evaluations, d1-ultra delivers higher accuracy and better-calibrated probabilities at the same price, and leads other decision models on the Artificial Analysis Decision Index by at least 8 points.

Liquid AI released d1-ultra, a multimodal decision model priced at $0.042 per million input tokens on the Liquid API. It targets harder, messier real-world cases such as spotting a defect on a circuit board against its spec or checking a damage claim against a customer photo, and answers three question types: noul, Choice, and Score. Compared with the previous d1 and Jev 1.14 across 112 unique evaluations, d1-ultra delivers higher accuracy and better-calibrated probabilities at the same price, and leads other decision models on the Artificial Analysis Decision Index by at least 8 points.

Liquid AI released LFM2.5-24B-A2B, a 24B-parameter reasoning model with just 2B active parameters per token, built for fast private agents on-device. Versus its predecessor LFM2-24B-A2B, it extends context from 32,768 to 131,072 tokens, doubles the vocabulary from 65K to 128K, scales pretraining from 17T to 33T tokens, and becomes reasoning-only, emitting an explicit chain of thought before its answer. It matches or leads Qwen3.6-35B-A3B, which has about 50% more total and active parameters, on five of eight tool-use and agentic benchmarks, scoring 27.69 versus 8.72 on AutomationBench, and decodes 184 tokens/s on an Apple M5 Max and over 21K output tokens/s on a single NVIDIA B300 GPU.

Liquid AI released LFM2.5-24B-A2B, a 24B-parameter reasoning model with just 2B active parameters per token, built for fast private agents on-device. Versus its predecessor LFM2-24B-A2B, it extends context from 32,768 to 131,072 tokens, doubles the vocabulary from 65K to 128K, scales pretraining from 17T to 33T tokens, and becomes reasoning-only, emitting an explicit chain of thought before its answer. It matches or leads Qwen3.6-35B-A3B, which has about 50% more total and active parameters, on five of eight tool-use and agentic benchmarks, scoring 27.69 versus 8.72 on AutomationBench, and decodes 184 tokens/s on an Apple M5 Max and over 21K output tokens/s on a single NVIDIA B300 GPU.

Liquid AI released LFM2.5-24B-A2B, a reasoning agent model with 24B total parameters but only 2B active per token, built to run locally on device. It keeps the predecessor LFM2-24B-A2B architecture while growing context from 32K to 131K tokens and vocabulary from 65K to 128K, and it now emits an explicit chain of thought before answering. Across 16 benchmarks it competes with Qwen3.6-35B-A3B, which has roughly 50% more total and active parameters: 48.09 versus 38.19 on LiveClaw and 27.69 versus 8.72 on AutomationBench, while decoding 184 tokens/s on an M5 Max.

Liquid AI released LFM2.5-24B-A2B, a reasoning agent model with 24B total parameters but only 2B active per token, built to run locally on device. It keeps the predecessor LFM2-24B-A2B architecture while growing context from 32K to 131K tokens and vocabulary from 65K to 128K, and it now emits an explicit chain of thought before answering. Across 16 benchmarks it competes with Qwen3.6-35B-A3B, which has roughly 50% more total and active parameters: 48.09 versus 38.19 on LiveClaw and 27.69 versus 8.72 on AutomationBench, while decoding 184 tokens/s on an M5 Max.