https://llmobile.kavents.com/tags/wearables/A concise news ticker covering AI on mobile devices, local models, apps and hardware.en-GBhttps://llmobile.kavents.com/ticker/pixel-watch-5-offline-gemini/https://llmobile.kavents.com/ticker/pixel-watch-5-offline-gemini/Wed, 12 Aug 2026 19:30:00 +0200Gemini Intelligence is coming to the Pixel Watch 5, 9to5Google reports. According to the report, offline Gemini commands on the watch use a separate on-device model when the phone or an internet connection is unavailable. Those commands cover timers and alarms, brightness and modes, music control, opening apps and starting workouts. On-device smart replies now offer three responses instead of one, which the report attributes to a Gemini Nano upgrade that makes them 50 percent faster. Other parts of the feature set depend on a connection. Proactive Suggestions, formerly Magic Cue, are generated on a paired Pixel 11 and bridged to the watch, and Personal Intelligence draws on Gmail, Calendar and Keep. The update also brings a new At a Glance space on the watch face for timers, workouts, music, flight details and navigation. Source: https://9to5google.com/2026/08/12/pixel-watch-5-gemini-intelligence/ Read the article: https://llmobile.kavents.com/ticker/pixel-watch-5-offline-gemini/GooglePixelWearablesGemini Nanohttps://llmobile.kavents.com/ticker/qualcomm-amon-agents-new-app/https://llmobile.kavents.com/ticker/qualcomm-amon-agents-new-app/Tue, 16 Jun 2026 07:08:00 +0200Qualcomm chief executive Cristiano Amon says the company is working on more than 40 designs of new AI devices, CNBC reports from an interview on its “The Tech Download” podcast. He listed jewellery, earbuds with cameras, pins and watches among the form factors. Amon described a shift in what devices are organised around: The phone is around the agent. The new classes of devices … are going to be around the agent as well. And the agent will be the one that will understand human intentions and will do things for you, so there is a shift in what the center of gravity is. He said phones will not disappear. On software, he told CNBC that apps are “not dead” but “are going to change”, adding: “Those agents are going to be the new app.” His example was an agent that retrieves banking transaction details without the user navigating the app. On smart glasses, Amon put current shipments in the “order of multiple tens of millions” a year and said that in “a couple of years” the category could reach the “order of hundreds of millions of glasses and could become as big as smartphones”. CNBC cites Counterpoint Research figures of 1.26 billion smartphones shipped in 2025, about 3 percent above the prior year. Amon linked the entry of AI companies into hardware, such as OpenAI’s purchase of Jony Ive’s startup io, to both agent endpoints and training data, saying the devices will gather data on a scale that is “exponentially larger” than the data used to train current models. He said Qualcomm’s chip roadmap is being reworked in response: “An entire roadmap, because I believe none of the devices we have today are prepared for the future.” The full interview is available as a video on CNBC. Source: https://www.cnbc.com/2026/06/16/qualcomm-ceo-ai-devices-agents.html Read the article: https://llmobile.kavents.com/ticker/qualcomm-amon-agents-new-app/QualcommAgentsWearablesSmart glasseshttps://llmobile.kavents.com/ticker/generative-ai-at-the-edge-queue/https://llmobile.kavents.com/ticker/generative-ai-at-the-edge-queue/Wed, 21 May 2025 18:00:00 +0200Vijay Janapa Reddi of Harvard University published an article in ACM Queue on May 21, 2025, arguing that generative models reach phones, glasses and robots only if the field stops chasing parameter counts and starts designing for deployment. He projects more than 50 billion edge devices by 2030 and writes that today’s frontier models, often hundreds of billions of parameters, cannot leave the data centre. His deployment matrix places only models under 1B parameters in the viable column for true edge hardware, puts medium models of 1B to 10B parameters within reach of high-end phones and single GPUs, and confines anything larger to servers or the cloud. Reddi grounds that in memory first. He writes that a 6B parameter model at half precision needs roughly 12 GB for weights alone, far above typical mobile RAM, that even a 1B model at about 2 GB can strain some devices, and that small models usually occupy 100 MB to 2 GB. He puts the latency bar at around 50 ms for an augmented reality translation to feel instantaneous and under a second for a dialogue assistant to feel fluid, and he notes that batching across users, which keeps cloud serving efficient, does not apply when requests arrive one at a time. On energy he cites an int8 quantised model using about five times less energy per query than an FP16 one on the same hardware, and says MLPerf Power results suggest model energy efficiency is plateauing even as hardware improves. Reddi frames the whole problem as a data-model-compute triangle, where scarce local data, limited compute and the need for compact models each worsen the others, and he argues compression alone will not close the gap, since smaller models hallucinate more and can lose their refusal behaviour when quantised hard. He proposes measuring capability and cost together through metrics such as hallucinations per watt-hour, running a model on factual question answering until it consumes 1 Wh and counting the wrong statements, and toxicity per token normalised by model size. The article ran in Queue volume 23, issue 2, and the full text is free to read on queue.acm.org. Source: https://dl.acm.org/doi/abs/10.1145/3733702 Read the article: https://llmobile.kavents.com/ticker/generative-ai-at-the-edge-queue/ResearchMemoryQuantisationNPUWearableshttps://llmobile.kavents.com/ticker/qwen2/https://llmobile.kavents.com/ticker/qwen2/Mon, 15 Jul 2024 14:35:00 +0200Alibaba’s Qwen Team published the Qwen2 Technical Report on July 15, 2024, covering five models from 0.5B to 72B parameters. The report states that the two smallest, Qwen2-0.5B and Qwen2-1.5B, are designed for easy deployment on portable devices such as smartphones, earphones and smart glasses, while the larger sizes target GPUs. Qwen2-1.5B scores 56.5 on MMLU, a multiple-choice knowledge test, against 52.7 for Microsoft’s Phi-2, which the same table credits with about twice the non-embedding parameters. Both small sizes tie the input embedding to the output layer, so a single weight matrix does both jobs and the shared vocabulary of 151,646 tokens is stored once rather than twice. The report puts non-embedding parameters at 0.3B for Qwen2-0.5B and 1.2B for Qwen2-1.5B. All five models use grouped query attention, where several query heads share one set of key and value heads, with 14 query heads over 2 key-value heads at 0.5B and 12 over 2 at 1.5B, which the authors say lowers the key-value cache size per token against Qwen1.5 and with it the memory footprint during long-context inference. Alibaba trained Qwen2-0.5B on a 12 trillion token dataset and the other dense models on a 7 trillion token one, reporting that the larger corpus did not pay off at the bigger sizes. For the instruction-tuned versions the report gives Qwen2-1.5B-Instruct 52.4 on MMLU, 61.6 on the GSM8K grade-school maths set and 29.0 on IFEval, which checks whether a model obeys explicit formatting instructions, against 37.9, 40.1 and 20.0 for Qwen2-0.5B-Instruct. The authors attribute the gain over the similarly sized Qwen1.5 models mainly to that data scaling. Context length differs across the family. The authors tested retrieval from long documents with Needle in a Haystack and report that the two small models handle 32K tokens, Qwen2-57B-A14B-Instruct 64K, and the 7B and 72B instruction-tuned models 128K with the YARN extension applied. The pre-training data covers approximately 30 languages, among them English, Chinese, Spanish, French, German, Arabic, Russian, Korean, Japanese, Thai and Vietnamese. Alibaba put the weights on Hugging Face and ModelScope. The Qwen2 blog post of June 7, 2024 states that Qwen2-0.5B, 1.5B, 7B and 57B-A14B carry the Apache 2.0 license while Qwen2-72B and its instruction-tuned variant stay on Alibaba’s own Qianwen License, and it names MLX, llama.cpp, Ollama and LM Studio among the projects that run the models locally. Source: https://arxiv.org/abs/2407.10671 Read the article: https://llmobile.kavents.com/ticker/qwen2/AlibabaQwenOpen weightsResearchWearables