#端侧

Spent some time experimenting with MiniCPM-V4.7 35B-A3B and built this Neo-Brutalist portfolio UI, featuring bold colors, chunky layouts, and a slightly unconventional visual style. What makes this experiment interesting is the model behind it. OpenBMB’s MiniCPM-V 4.7 is pushing multimodal AI toward more practical deployment on everyday hardware. With 35B total parameters but only 3B activated at a time, MiniCPM-V 4.7 35B-A3B delivers 17+ tokens/s on iPad Pro M4 (16GB) and 13+ tokens/s on iPhone 17 Pro Max, using just ~3GB of memory in its optimized mobile setup. 🤯 There’s also a compact 1B variant designed for tasks like OCR, chart, math, and general understanding. Both variants support a 256K context window, and the model weights, inference code, and fine-tuning code are open source. For me, the interesting part of multimodal AI is its potential to work with visual information beyond text, and to make those capabilities more accessible through efficient, flexible deployment. Explore MiniCPM-V 4.7 from @OpenBMB: GitHub：http://github.com/OpenBMB/MiniCPM-V Hugging Face：http://huggingface.co/collections/openbmb/minicpm-v-47

I built an automated report generator that runs entirely on my own machine. Input: structured data + charts. Output: a full long-form report — earnings interpretation, weekly summaries, whatever you need. No API. No cloud. No monthly bill. -> Here's MiniCPM-V 4.7 35B-A3B doing exactly that 👇 It read the charts AND the dense tables in one pass, then streamed out analyst-grade markdown: scorecard, segment breakdown, margin drivers, cash flow, even a bull/bear case. The part that got me: it didn't just OCR numbers. It connected them — input costs → margin pressure → guidance risk. That's the jump from "text extraction" to "actual analysis." Why this works locally: → 35B total params, only ~3B active (MoE) → Efficient visual encoding keeps multi-chart inputs cheap → Structured markdown output — goes straight into your doc pipeline Try it GitHub: http://github.com/OpenBMB/MiniCPM-V Hugging Face: https://huggingface.co/collections/openbmb/minicpm-v-47

Run @AntLingAGI speech models locally 🎙️ Ming-3.0 is 5 open models that talk, listen and write down what you say Its voice model now ranks #1 among open models on Artificial Analysis starting at just 0.5B Run AI models locally -> https://atomic.chat

Perplexity Flash takes the crown! ⚡ In our tests, it matched Wald's accuracy at nearly twice the speed on a 16 GB Mac mini. Synthetic agent decisions, real email threads, prompt injections: see why Flash is our new pick for email-completion checks. 👇 https://x.com/i/article/2111461650440110080

NVIDIA RTX Spark is here. Build AI locally, bring your creative ideas to life, and take your favorite games along. A new beginning for @Windows PCs starts today.

MiniCPM-V 4.7 is now open source on ModelScope: a 35B-A3B MoE multimodal model built to run on-device without compromising performance. Apache 2.0. 🤖 https://modelscope.ai/collections/OpenBMB/MiniCPM-V-47 📱 First open-source MoE with smooth, lossless on-device inference, running at 13+ tokens/s on iPhone 17 Pro Max. 🔥 New Pareto frontier: in Instruct mode, it reaches a higher overall score in less inference time than Qwen3.6 35B-A3B. ⚡ Mixed 4x/16x visual token compression lets developers balance visual detail and inference cost per task. 🛠️ Two sizes (35B-A3B and 1B) with FP8, GPTQ, AWQ, BNB, and GGUF variants. Works with SGLang, vLLM, llama.cpp, Ollama, SWIFT, and LLaMA-Factory. 💻 Runs on iOS, Android, and HarmonyOS, with day-0 support for Rockchip, Houmo, Intel, AMD, and Arm.