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Sunday

  1. Story1 reports · 1 facts

    OpenBMB showcases MiniCPM-V 4.7 1B running on-device on an ordinary iPhone

    OpenBMB (ModelBest) showcased MiniCPM-V 4.7 1B running locally on an ordinary iPhone via OnDeviceLLM. Related reports say the model is no longer limited to the 12GB-memory Pro Max models, and can complete vision tasks such as reading receipts and calculating totals, extracting flyer information, and understanding screenshots offline on-device, and can also be used in airplane mode. Currently this information comes from the same occurrence evidence from a single source, with no subsequent independent developments yet.

    OpenBMB (ModelBest) showcased MiniCPM-V 4.7 1B running locally on an ordinary iPhone via OnDeviceLLM. Related reports say the model is no longer limited to the 12GB-memory Pro Max models, and can complete vision tasks such as reading receipts and calculating totals, extracting flyer information, and understanding screenshots offline on-device, and can also be used in airplane mode. Currently this information comes from the same occurrence evidence from a single source, with no subsequent indepen

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  2. Story1 reports · 1 facts

    Matt Turck says open-source voice AI welcomes a small on-device SOTA TTS model suite

    Matt Turck reposted and commented that a small on-device SOTA text-to-speech model suite has emerged in the open-source voice AI space. According to related reports, Gradium has open-sourced the Phonon on-device speech synthesis model suite, with all model parameters under 50m, focusing on multilingual naturalness and accuracy, and claiming to offer the broadest inference support to date; among them, Phonon-31m is released under the Apache 2.0 license. Currently, this news has been reposted by Matt Turck, with no further developments yet.

    Matt Turck reposted and commented that a small on-device SOTA text-to-speech model suite has emerged in the open-source voice AI space. According to related reports, Gradium has open-sourced the Phonon on-device speech synthesis model suite, with all model parameters under 50m, focusing on multilingual naturalness and accuracy, and claiming to offer the broadest inference support to date; among them, Phonon-31m is released under the Apache 2.0 license. Currently, this news has been reposted by M

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  3. FactRelated story

    Matt Turck says open-source voice AI welcomes a small on-device SOTA TTS model suite

    Matt Turck reposted and commented that a small on-device SOTA text-to-speech model suite has emerged in the open-source voice AI space. According to related reports, Gradium has open-sourced the Phonon on-device speech synthesis model suite, with all model parameters under 50m, focusing on multilingual naturalness and accuracy, and claiming to offer the broadest inference support to date; among them, Phonon-31m is released under the Apache 2.0 license. Currently, this news has been reposted by Matt Turck, with no further developments yet.

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    View story:Matt Turck says open-source voice AI welcomes a small on-device SOTA TTS model suite

  4. Story1 reports · 1 facts

    Meta compresses its Agent model Muse Glimmer 30B to run on-device

    On October 20, 2026, Meta released the technical report for Muse Glimmer, a 30B open-weight model aimed at agent tasks on consumer-grade hardware, running at 4-bit precision on a single consumer GPU. The reporter adds that the model leads open-source models of the same scale in tool calling and multi-step planning, surpassing Qwen3.6-27B and Gemma4-31B on some metrics; after 4-bit quantization it is about 17GB, targeting devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding. There are currently no subsequent developments.

    On October 20, 2026, Meta released the technical report for Muse Glimmer, a 30B open-weight model aimed at agent tasks on consumer-grade hardware, running at 4-bit precision on a single consumer GPU. The reporter adds that the model leads open-source models of the same scale in tool calling and multi-step planning, surpassing Qwen3.6-27B and Gemma4-31B on some metrics; after 4-bit quantization it is about 17GB, targeting devices with 24GB of VRAM, and uses DFlash speculative decoding to reach ab

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Wednesday

  1. FactRelated story

    OpenBMB demonstrates MiniCPM-V 4.7 1B running on-device on iPhone

    OpenBMB (ModelBest) showcased MiniCPM-V 4.7 1B running locally on an ordinary iPhone via OnDeviceLLM. Related reports say the model is no longer limited to the 12GB-memory Pro Max models, and can complete vision tasks such as reading receipts and calculating totals, extracting flyer information, and understanding screenshots offline on-device, and can also be used in airplane mode. Currently this information comes from the same occurrence evidence from a single source, with no subsequent independent developments yet.

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    View story:OpenBMB showcases MiniCPM-V 4.7 1B running on-device on an ordinary iPhone

Tuesday

  1. FactRelated story

    Meta compresses its agent model Muse Glimmer 30B to run on-device

    On October 20, 2026, Meta released the technical report for Muse Glimmer, a 30B open-weight model aimed at agent tasks on consumer-grade hardware, running at 4-bit precision on a single consumer GPU. The reporter adds that the model leads open-source models of the same scale in tool calling and multi-step planning, surpassing Qwen3.6-27B and Gemma4-31B on some metrics; after 4-bit quantization it is about 17GB, targeting devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding. There are currently no subsequent developments.

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    View story:Meta compresses its Agent model Muse Glimmer 30B to run on-device

Friday

Thursday

Wednesday

  5. Story3 reports · 2 facts

    Liquid AI Releases LFM2.5-24B-A2B On-Device Reasoning Model

    On October 13, 2026, Liquid AI released the LFM2.5-24B-A2B on-device agent model. With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretraining data from 17T to 33T tokens; it leads or ties Qwen3.6-35B-A3B on five of eight agent and tool-calling benchmarks, scoring 27.69 versus 8.72 on AutomationBench and 48.09 versus 38.19 on LiveClaw; it decodes at 184 tokens/s on an Apple M5 Max and outputs over 21K tokens/s under high concurrency on a single NVIDIA B300 GPU. On October 14, OpenRouter announced that the model is now available, saying it reasons before answering and targets agent and tool-calling scenarios.

    On October 13, 2026, Liquid AI released the LFM2.5-24B-A2B on-device agent model. With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretrai

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Tuesday

  1. FactRelated story

    English translation is being prepared.

    On October 13, 2026, Liquid AI released the LFM2.5-24B-A2B on-device agent model. With 24B total parameters and only 2B activated per token, it is the largest and final model in the LFM2.5 family, and both the base and post-trained versions have open weights on Hugging Face and Playground. According to the official blog, it uses a pure reasoning mode with explicit chain-of-thought, expands the context window from 32,768 to 131,072 tokens, grows the vocabulary from 65K to 128K, and scales pretraining data from 17T to 33T tokens; it leads or ties Qwen3.6-35B-A3B on five of eight agent and tool-calling benchmarks, scoring 27.69 versus 8.72 on AutomationBench and 48.09 versus 38.19 on LiveClaw; it decodes at 184 tokens/s on an Apple M5 Max and outputs over 21K tokens/s under high concurrency on a single NVIDIA B300 GPU. On October 14, OpenRouter announced that the model is now available, saying it reasons before answering and targets agent and tool-calling scenarios.

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    View story:Liquid AI Releases LFM2.5-24B-A2B On-Device Reasoning Model

Sunday

Thursday