Top Tweets for #Executorch
Mergen Nachin and Digant Desai (Meta) present "Building Portable, Composable Local Agents with ExecuTorch" at PyTorch Conference. Discover offline-first edge execution strategies.
Oct 20 | 5:45 PM
#EdgeAI #ExecuTorch #Meta #OfflineFirst https://t.co/3qhvTT3BX2
Our Muse Glimmer technical report is out!
Your AI agent, on your own machine. It's open, built for consumer hardware, and with #ExecuTorch it runs fast on your Mac or NVIDIA GPU.
Grateful for the opportunity to be a core contributor on this amazing team at @AIatMeta.
Today we published a technical report on Muse Glimmer, our 30B open-weight model built for agentic tasks on consumer hardware.
It runs at practical speeds on a Mac or PC with a single consumer GPU at 4-bit precision, and leads comparably-sized open models on tool use and multi-step planning.
Read about the model architecture, distillation recipe, quantization and more: https://t.co/H7n9qeDhxn
#ExecuTorch is officially out of this world! 😁
Two Meta Quest 3 headsets were launched aboard NASA’s 35th SpaceX Commercial Resupply Services mission and arrived at the International Space Station today. 🚀
As part of the European Space Agency’s Mixed Reality Facility, astronauts will use them to rehearse spacewalks before venturing outside the station.
And, like all Meta Quest 3 headsets, they run AI models on-device using ExecuTorch!
Over the weekend I got voice, image and text working fully on-device on my iPhone with #ExecuTorch
- #Gemma4 for text
- Whisper + 🔊 Kokoro for voice
- LFM 2.5 Vision for images
No server. No API. Works in airplane mode.
@GoogleDeepMind @PyTorch @swmansion
What an incredible journey! Grateful to be part of this effort, to work on this model, and to land day-0 support in #ExecuTorch. 🚀
Today @AIatMeta introduced Muse Glimmer, an open-weight, 30-billion-parameter model distilled from Meta’s Muse Spark for on-device agentic workflows. Alongside, ExecuTorch is adding end-to-end support for running Muse Glimmer on @NVIDIA GPUs and Macs with @Apple silicon.
Why ExecuTorch? ExecuTorch offers a smarter, native approach:
⚡ Build in PyTorch: Implement your model and decoding strategies right in PyTorch as you normally would
⚡ Seamless Export: Export directly to ExecuTorch when you're ready for deployment
⚡ Automated Optimization: The framework automatically handles backend-specific lowering (Triton on CUDA, MLX-native, or Metal on Apple silicon) and uses ahead-of-time compilation to optimize the full execution path end-to-end
Learn more: https://t.co/ABzlzreEJz
5/ weights on hugging face now. running this week through ollama, LM Studio, vllm, sglang, together, fireworks, and openrouter, with llama.cpp, MLX, and executorch.
evaluated under meta's advanced AI scaling framework and cleared for open-weight release.
M1 MaxでQwen3が4.5倍速は大きいな
MacユーザーのLLM環境が一段上がった感ある
ExecuTorchのMLX delegateでQwen3を動かす――M1 Maxで最大4.52倍高速
#ExecuTorch #Qwen3高速化 #M1MaxLLM
https://t.co/m3o8m7j14F
#ExecuTorch addresses fragmented native deployment for #AI agents as a #PyTorch native platform. It enables voice models across CPU, GPU, and NPU on Android, iOS, Linux, macOS & Windows
🔗 https://t.co/NeQQyUniL4
PyTorch at the micro-edge? Yes.
See how ExecuTorch brings PyTorch models to Arm microcontrollers—quantized, compiled, and running on a Corstone-320 + Ethos-U NPU (via FVP). 🔗 https://t.co/plbiWAAu85
From training to deployment, end to end.
#PyTorch #ExecuTorch #EdgeAI #TinyML #Arm
Our paper #ExecuTorch - A Unified PyTorch Solution to Run #ML Models #OnDevice is accepted to appear at #MLSys 2026. Excited!
1/ ExecuTorch 1.0 just dropped: a PyTorch-native engine for phones, wearables, embedded—and even laptops. Think LLMs + multimodal running locally with a tiny runtime, no conversions or rewrites. #ExecuTorch #EdgeAI
🚀 ExecuTorch export: .pte + XNNPACK for mobile/edge YOLO11/YOLO26. 🔧 VisualAI CLIP fix. vv8.3.220
#Ultralytics #ExecuTorch
https://t.co/OtAqPCjQ68
No flagship? No GPU? No problem.
ExecuTorch 0.7 brings KleidiAI acceleration to billions of Arm devices—including 3–5 year-old phones and Raspberry Pi 5.
On-device AI, powered by Arm CPUs and SDOT.
🔗 https://t.co/17Dx7leuj0
#PyTorch #ExecuTorch #EdgeAI
Big updates to #TorchAO low-bit operators for Arm CPU: dynamic kernel selection, KleidiAI integration, and quantized tied embeddings—boosting performance across #PyTorch, including #ExecuTorch for on-device inference.
🔗 https://t.co/ApwXwB6z7q
SmolLM3-3B-8da4w: With #TorchAO & optimum-executorch, quantizing and exporting for mobile is a breeze.
Now ready for on-device deployment with #ExecuTorch, running at 15 tokens/sec on Galaxy S22. 🔗 Model card with recipes + checkpoints: https://t.co/mb0Zy0keuf
#EdgeAI #PyTorch
Breaking new ground in mobile AI! React Native ExecuTorch stands out as a powerful declarative library. It empowers developers to run sophisticated AI models directly on devices! @kacperkapusciak #ExecuTorch
https://t.co/93pHKiPBX5
Das neue @iXmagazin 2/ 2025 bringt Teil 1 meines Tutorials zu ExecuTorch. Das Framework und API macht große LLMs passend für Smartphones und Edge-Devices. Vielen Dank an die Redaktion. #executorch #ix #heise #LLM
https://t.co/gPIiEG0MGa
Run LLMs on-device using @expo react native and #ExecuTorch 🔥 with this #oss library by @swmansion!
Demo & code 👇
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