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Only Models#On-device
TodayNovember 13Friday2 items
  1. Hacker News · AI88

    Edge0 open-sources Edge0-145B and Edge0-120B on-device models with inference framework

    Edge0 open-sourced Edge0-145B and Edge0-120B along with its unified inference framework and deployment tools, claiming Edge0-145B is the first open-source 145B-parameter on-device model. The company reports 29GB peak memory, 1,100+ tokens/s prefill, 80+ tokens/s decode and roughly 120W typical power on a compatible laptop, with 128K–256K context. It cites internal scores of 64.6 on DeepSWE 1.1, 86.52 on Terminal-Bench 2.1, 90.4 on GPQA-Diamond and 80.6 on IFBench, and says weights are on Hugging Face with day-one support for macOS, Windows and Linux.

    Why it matters: Edge0 open-sources a 145B on-device model that reports 29GB peak memory and 80+ tokens/s decode, offering a concrete data point on how far local inference can scale.

  2. MarkTechPost52

    Liquid AI Releases LiquidCodec, a 74M-Parameter Streaming Audio Codec

    Liquid AI released LiquidCodec, a roughly 74M-parameter streaming speech tokenizer that powers its LFM3-Audio-2B and LFM3-Omni-2B models, with weights published on Hugging Face. The codec compresses 24 kHz audio into 12 token streams at 12.5 Hz frames and runs on a single CPU thread, where Liquid AI reports about 361x real-time encoding and 87x decoding. Liquid AI says it beat X-Codec 2, NanoCodec and Mimi on ViSQOL and mel distance across all 8 test sets, though NanoCodec still led PESQ (WB) on 7 of 8 sets.

November 11Wednesday
  1. Hugging Face Blog42

    Tether AI Research Releases BotPsy: A 0.8B On-Device Agentic Model Built on Qwen3.5-0.8B

    Tether AI Research released BotPsy, a 0.8B-parameter agentic model built on Qwen3.5-0.8B for on-device personal-assistant tool use. It roughly doubles its base model's agentic scores, with BFCL v4 rising from 23.4 to 44.0 and τ-bench from 23.2 to 51.5, and beats every model up to 1.2B parameters tested across six tool-use benchmarks. It also cuts unsafe responses to 21% from 36% and uses about 3× fewer reasoning tokens on GPQA Diamond.

November 9Monday
  1. Zyphra Research78

    Zyphra releases ZONOS3-Mini, an Apache 2.0 zero-shot voice-cloning TTS model that runs on-device

    Zyphra released ZONOS3-Mini, a sparse MoE text-to-speech model with 100M active and 800M total parameters under Apache 2.0, trained on 112 NVIDIA H100s in 2.5 days. It reaches under 570 ms time-to-first-audio in the browser on a Pixel 9 and under 150 ms on an M3 MacBook Air, with runtimes for WebGPU, SGLang, vLLM and GGML. The model adds control over identity, accent, quality, mood and delivery, including word-level steering, and Zyphra reports it ranks first among open models on its new ZTTS2-Eval benchmark.

    Why it matters: Zyphra's compact MoE TTS model pairs zero-shot voice cloning with word-level delivery control while running in real time on phones and laptops.

November 5Thursday
  1. Preferred Networks News71

    PFN releases PLaMo 3 Translate open-weight translation LLM for edge devices

    Preferred Networks released PLaMo 3 Translate, a translation-specialized LLM used in its PLaMo Translation service, as an open-weight model on Hugging Face on November 5. 8B, 2B, 8B and 31B) and supports 53 languages including Japanese and English, running on smartphones, laptops and internal servers without a cloud connection.

    Why it matters: PFN releases a translation-specialized LLM in four sizes from 0.8B to 31B, letting companies and individuals run 53-language translation on-device without cloud access.

  2. Artificial Analysis Articles58

    Nanbeige releases Nanbeige4.3-33B, scoring 20 on the Artificial Analysis Intelligence Index

    Nanbeige has released Nanbeige4.3-33B, an open-weights dense reasoning model under Apache 2.0 that scores 20 on the Artificial Analysis Intelligence Index v5, matching Qwen3.8 27B (xhigh) at 20. The 33B model has a 524K-token context window, text-only input and output, and no confirmed public APIs, and it scores 30% on Terminal-Bench 4.0 versus 6% for Qwen3.8 27B while trailing on HLE-Diamond (7% vs. 15%). On AA-Omniscience v1.1 it records an 18% hallucination rate versus 29% for Qwen3.8 27B, with a 71% decline rate, and it uses 158K output tokens per Intelligence Index task versus 110K for Qwen3.8 27B.