与「WebGPU」相关的搜索结果

Lightning-fast Multilingual TTS that runs entirely on your device! Supertonic is a lightning-fast, on-device multilingual text-to-speech system designed for local inference with minimal overhead. The model runs via ONNX Runtime with 66M parameters. Generates speech up to 167x faster than real-time on consumer hardware. Complete privacy, zero network dependency, all processing happens locally. Supports 31 languages including English, Korean, Spanish, Portuguese, French, German, Japanese, Chinese, Arabic, Dutch, and more. Natural text handling without pre-processing. Directly processes numbers, dates, currency, abbreviations, and complex expressions. Performance on M4 Pro CPU: 1263 characters per second for long text, real-time factor of 0.012. WebGPU mode reaches 2509 characters per second. RTX 4090 hits 12,164 characters per second. Natural text handling works on financial expressions ("$5.2M" pronounced correctly as "five point two million dollars"), time and dates ("4:45 PM on Wed, Apr 3, 2024"), phone numbers with extensions, and technical units with abbreviations. All without phonetic annotations or text normalization. Voice Builder lets you turn your voice into a deployable TTS model with permanent ownership and edge-native deployment. Key capabilities: • Ultra-lightweight (66M parameters) • On-device inference with zero latency • Natural text handling without pre-processing • 31-language multilingual support • Cross-platform via ONNX Runtime • Up to 167x faster than real-time • Complete privacy - all local processing • Custom voice creation with Voice Builder • Expression tags for natural human nuance It's 100% Open source I've shared the link in the replies!

If you love fine-tuning open-source models (like me), then listen. > Start with 1B, 2B, 4B, and 8B models. (Don't start with a 27B model or bigger at first.) > Use WebGPU providers. I use Google Colab Pro for any model smaller than 9B. A single A100 80GB costs around $0.60/hr, which is cheap. Enough for small models. > Don’t buy GPUs unless you fine-tune 7 to 10 models. You'll understand the nitty-gritty in the process. > Use Codex 5.5 × DeepSeek v4 Pro to create datasets. Codex to plan, DeepSeek v4 Pro to generate rows. > Use Unsloth's instruct models as a base from Hugging Face. Yes, there are others too, but Unsloth also provides fast fine-tuning notebooks. > Use Unsloth's fine-tuning notebooks as a reference. Paste them into Codex, and Codex will write a custom notebook with the configs you need. > Spend 1 day learning about: - SFT (supervised fine-tuning) - RL training (GRPO, DPO, PPO, etc.) - LoRA / QLoRA training - Quantization and types - Local inference engines (llama.cpp) - KV cache and prompt cache > Just get started. Claude, Codex, and ChatGPT can design a step-by-step plan for how you can fine-tune your first AI model. Future tech is moving toward small 5B to 15B ELMs (Expert Language Models) rather than general 1T LLMs. So fine-tuning is an important skill that anyone can acquire today. Tune models, test them, use them. Then fine-tune for companies and make a career out of it. (Companies pay $50k+ to fine-tune models on their data so they can get personalized AI models.) Shoot your questions below. I'll be sharing in-depth raw findings about this topic in the coming days.

建筑师们肯定会对此感到“抓狂”。有人刚刚开源了一个完全运行在浏览器中的全功能 3D 建筑编辑器。无需 AutoCAD，无需 Revit，也不需要每年 5,000 美元的授权费用。它被称为 Pascal Editor。它基于 React Three Fiber 和 WebGPU 构建——这意味着它直接在你的 GPU 上进行渲染，速度接近原生。以下是它的核心功能： → 一个完整的建筑/楼层/墙体/区域层级结构，支持实时编辑 → ECS 风格的架构，每个对象都通过 GPU 驱动的系统进行更新 → 集成了完整撤销/重做功能的 Zustand 状态管理 → 基于 Next.js 的前端，因此它作为 Web 应用部署，而非桌面安装版 → 脏节点追踪（Dirty node tracking）——仅重新渲染发生变化的部分，而非整个场景 最令人惊叹的部分在于：你可以对单个建筑楼层进行堆叠、拆解或单独查看。选择一个区域，拖动一面墙，重塑一个楼板——全部在 3D 环境中，全部在浏览器中完成。 建筑事务所为实现这种工作流的 BIM 软件，每个席位的费用高达 5 万美元以上。而这个是免费的。100% 开源。

WICG 推出 HTML in Canvas 标准，但开发者千万别高兴得太早。 虽然 DOM 元素能直接嵌入 WebGL/WebGPU，但 DOM 节点与 GPU 渲染的同步成本，以及浏览器兼容性的落地周期，依然是巨大的技术坑。 你会为了便捷而牺牲渲染性能吗？

Web 动画工程师和 Web 游戏开发者的危机感，现在该上场了。 GPT-5.5 已经能直接处理 WebGPU 和 WebGL 渲染逻辑。 以前需要深厚数学功底和图形学经验才能搞定的复杂交互，现在可能只需要一段 Prompt。 你会选择转行做架构，还是继续死磕图形学实现？

Bonsai 1-bit 把大模型的运行门槛直接打到了浏览器里。290MB 的体积，通过 WebGPU 就能在你的网页端跑出实用级的逻辑。 这意味着你不再需要租昂贵的 A100 显卡，只要有个浏览器，本地算力就能直接接手推理任务。这种极端的 1-bit 量化压缩，正在把 AI 的部署成本从‘云端订阅制’强行拉回‘本地零成本’。

《WebGPU 为什么重要》，推荐这篇长文。 操作系统的图形 API，目前是不统一的：Windows 是 DirectX，苹果是 Metal，Linux 是 Vulkan。而新发布的 WebGPU 是跨平台的图形编程接口。