←Search results

English text is being prepared.

By relevance · Total 86 results Clear search

Saturday

  1. Story1 reports · 1 facts

    NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

    NVIDIA releases the on-device small model Nemotron-3.5-Bolt-E4B. A report on October 23, 2026 states that the model can run locally on devices with 6GB of memory, targeting on-device devices such as phones; it is trained based on Gemma 4, supports vision, and Unsloth simultaneously provides the GGUF file and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools. A follow-up report on October 24 states that the model has 8B total parameters, 4B effective parameters, and a 64K context, and is post-trained using only reinforcement learning based on Google Gemma 4 E4B; the Q4_K_M version occupies 3.1 to 4.3 GB of VRAM on RTX and can run on GeForce RTX, Jetson, and DGX Spark. The report also states that in NVIDIA's own testing, it leads Gemma 4 E4B, Qwen3.5-4B, and Nemotron 3 Nano 4B in 15 of 21 benchmarks, but Tau2-Bench is 55.1, trailing Qwen3.5-4B's 75.1.

    NVIDIA releases the on-device small model Nemotron-3.5-Bolt-E4B. A report on October 23, 2026 states that the model can run locally on devices with 6GB of memory, targeting on-device devices such as phones; it is trained based on Gemma 4, supports vision, and Unsloth simultaneously provides the GGUF file and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools. A follow-up report on October 24 states that the

    Matched fieldsTitle

Friday

  1. FactRelated story

    NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

    NVIDIA releases the on-device small model Nemotron-3.5-Bolt-E4B. A report on October 23, 2026 states that the model can run locally on devices with 6GB of memory, targeting on-device devices such as phones; it is trained based on Gemma 4, supports vision, and Unsloth simultaneously provides the GGUF file and a guide link. NVIDIA says it targets scenarios such as local assistants, interactive characters, and robots that can converse and call tools. A follow-up report on October 24 states that the model has 8B total parameters, 4B effective parameters, and a 64K context, and is post-trained using only reinforcement learning based on Google Gemma 4 E4B; the Q4_K_M version occupies 3.1 to 4.3 GB of VRAM on RTX and can run on GeForce RTX, Jetson, and DGX Spark. The report also states that in NVIDIA's own testing, it leads Gemma 4 E4B, Qwen3.5-4B, and Nemotron 3 Nano 4B in 15 of 21 benchmarks, but Tau2-Bench is 55.1, trailing Qwen3.5-4B's 75.1.

    Matched fieldsTitle

    View story:NVIDIA releases on-device small model Nemotron-3.5-Bolt-E4B

Sunday

  1. 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

    Matched fieldsTitle

  2. 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.

    Matched fieldsTitle

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

Wednesday

  3. Story2 reports · 1 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

    Matched fieldsTitle

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.

    Matched fieldsTitle

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

Sunday

Tuesday

Thursday

Tuesday

Monday