Compact model families such as LFM3, MedJev and distilled Qwen fine-tunes push AI to phones, laptops and collaborative hardware, reducing cloud dependency. The trend highlights small-parameter, task-specific models as a viable local inference path.
Liquid AI has introduced LFM3, a family of compact models from 1.7–2.2 billion parameters with a tri-hybrid architecture and llama.cpp support. The decision models skip token generation and return calibrated answers in a single forward pass. For on-p...
An open System One model runs locally on a laptop and reaches results close to GPT-4 on medical benchmarks, with well-controlled calibration on knowledge tests but less reliable patient-level predictions. A two-parameter recalibration method using 50...
Liquid AI has released a family of models ranging from 1.7 to 2.2 billion parameters, designed to run on phones, laptops, and edge devices. Beyond text, there are multimodal versions and decision models that return answers in a single pass. The commo...
CrowdGPT aims to train a one-billion-parameter LLM collaboratively, using participants' PCs instead of datacenters and publishing 100% of data and weights. The developer asks for feedback on incentives, from GPU time to inference credits. For now it ...
An independent project has shared an early 4-billion-parameter model for coding and 3D scene generation, fine-tuned from Qwen3.5-4B. GGUF checkpoints Q4_K_M and Q8_0 are available on Hugging Face while training continues. The author plans to update t...
The subscription economics of large LLMs is under pressure, while specialized models with a few billion parameters solve tasks like OCR and segmentation. The thesis: the future is local, with smaller models, easier training, and no data center power ...
