@xenovacom: We've just made a breakthrough: the first true omni model running 100% locally in a browser... fully end-to-end, with n…

X AI KOLs Timeline Models

Summary

Xenova 宣布突破:首个 100% 本地在浏览器中运行的真正全模态模型 LFM3-Omni-2B,借助自定义 WebGPU kernels 实现约 280 tok/s,支持视觉、音频和文本输入并生成交错的文本与音频输出。

We've just made a breakthrough: the first true omni model running 100% locally in a browser... fully end-to-end, with no separate ASR or TTS models needed. With custom WebGPU kernels, we're able to run LFM3-Omni-2B at ~280 tok/s on my M4 Max, accepting visual, audio, or text and producing interleaved text and audio tokens. Just 90 ms to the first token and 140 ms to the first audio. The future of on-device multimodal AI agents is going to be wild.
Original Article
View Cached Full Text

Cached at: 11/01/26, 01:40 AM

We’ve just made a breakthrough: the first true omni model running 100% locally in a browser… fully end-to-end, with no separate ASR or TTS models needed.

With custom WebGPU kernels, we’re able to run LFM3-Omni-2B at ~280 tok/s on my M4 Max, accepting visual, audio, or text and producing interleaved text and audio tokens. Just 90 ms to the first token and 140 ms to the first audio.

The future of on-device multimodal AI agents is going to be wild.

Similar Articles

LFM3-Omni-2B running locally in-browser on WebGPU

Reddit r/LocalLLaMA

LiquidAI 的 LFM3-Omni-2B 成为首个可在浏览器中通过 WebGPU 100% 本地运行的全模态模型,无需独立 ASR/TTS,在 M4 Max 上实现约 280 token/s、90ms 首 token 延迟。

@hank_aibtc: Amazing! Running Gemma 4 in the browser, on par with ChatGPT?! Completely zero server, zero data upload, offline, pure WebGPU local inference! Xenova has open-sourced all 27 custom WebGPU kernels written by Fable 5: - Gemma 4 E2B (2.3B parameters...)

X AI KOLs Timeline

The article introduces Xenova's open-sourcing of 27 custom WebGPU kernels, enabling Gemma 4 to run fully offline and locally in the browser at 255 tok/s, and discusses advantages like privacy and offline use. It also mentions FLUX.2's 3D generation capability.

An 80B MoE (Qwen3-Next, 48 GB GGUF) answering in a Chrome tab: llama.cpp's WebGPU backend, experts paged in from disk

Reddit r/LocalLLaMA

作者将上周的 MoE 专家按需从磁盘流式加载的方案接入 llama.cpp 的 WebGPU 后端,使 80B MoE 模型(Qwen3-Next,48GB GGUF)也能在 Chrome 标签页中运行,无需为每个模型手写引擎。该方案新增了 ggml-webgpu 的缓冲类型、slot 映射的 MUL_MAT_ID 着色器和驻留逻辑,实测在 M4 Pro MacBook 上以 2.9 tok/s 解码 80B 模型,较小模型可达 22 tok/s。