The trend towards running AI models directly on user devices (laptops, phones, edge) is accelerating, driven by compact models, quantization techniques, and specialized architectures. This shift aims to reduce cloud costs, enhance privacy, and enable immediate decision-making, despite ongoing challenges in runtime integration and agent performance.
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...
The Swedish startup Strawberry exits beta with an enterprise browser that embeds AI assistants. Agents run on the user's device, not on cloud servers, which can address data sovereignty demands. The company has raised $6 million and plans to scale ac...
ISTA-DASLab's release of GGUF quantized weights for DeepSeek-V4.1-Flash-1017 is not just a quantization milestone. It exposes the real bottleneck in local deployment: runtime integration. With llama.cpp still lacking official support and references p...
ISTA DASLab has released three GGUF builds of DeepSeek-V4.1-Flash-1017 with GSQ and RCO quantization, at 1.75 to 2.25 bits per weight over the 551.9 billion backbone parameters. A shared 110.6 GB Engram file stays memory-mapped on disk. The model run...
An experiment with a GGUF Q4 LLM in WebGL2 breaks on tokenization, not linear algebra. The advice to clone llama.cpp signals that in local and self-hosted deployments the differential lies not in the engine but in integration with data and processes....
ISTA-DASLab has published GGUF quants of DeepSeek-V4.1-Flash-1017 using the GSQ-RCO quantization approach. However, llama.cpp does not yet support the architecture: the relevant pull requests are still open. The model card mentions Strata, but the or...
A developer on Reddit describes an attempt to build a browser-based LLM inference engine using WebGL2 and GLSL, running a GGUF model in Q4 quantization. Tokenization becomes an endless debug hell. The advice: clone llama.cpp. AI-RADAR examines what 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 ...
A user tested LFM2.5-24-A2B locally. With llamaampere it hits 130 tokens/s on an RTX A4500 but loops and fails simple requests. In LM Studio chat stabilises, but on the Hermes task for a Garnet plugin it produces broken code, omits functionality, and...
Kitten-TTS 2 brings 1.58-bit ternary quantization to a 1.7 billion parameter model that runs in real time on CPU. Self-hosting becomes realistic without GPUs, shifting TCO from VRAM to software execution quality. For organizations under data sovereig...
KittenML has released code and weights for Kitten-TTS 2, a TTS model with 1.58-bit ternary quantization and 1.7 billion parameters. It runs in real time on CPU, performs zero-shot voice cloning from under five seconds of audio, supports more than 20 ...
An Apache-2.0 open source project offers an on-device voice front-end for local assistants: five models for text-based wake word, voiceprint, voice separation and personal VAD. The Rust engine runs in the browser, Python and Android; models take 7.5 ...
Specter combines quantization, speculative decoding, and expert offloading to speed up mixture-of-experts inference on a single GPU. A lightweight quantized draft model runs on-device and anticipates expert usage with high fidelity, reducing PCIe bot...
KRAFTON AI releases Raon-1 mini preview, a 30B MoE LLM with 4B active parameters, focused on Korean conversation and agentic workflows. With Apache 2.0, speculative decoding support, and NVFP4/MXFP8 quantization, inference can run on a single NVIDIA ...
