We wrote a full technical guide on how to train a bilingual (ES/EN) LLM from scratch: TinyQwen.
Covers: - Hybrid architecture based on Qwen3.5 - Pre-training with 15B tokens - Cost benchmark between H200 and B200 - Post-training with SFT + LoRA - Full code and data, open source
With ~$11 of compute on an H200 we ran an initial training run, enough to validate the full architecture and pipeline.
if you like it give the demo a little star and send a shoutout to : @MaxLSB@jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
AgentCPM-Explore🔥 on device agent foundation model released by OpenBMB openbmb/AgentCPM-Explore ✨ 4B - Apache2.0 ✨ Supports 100+ multi-turn environment interactions with search + verification ✨ Full training/inference stack is openly shared as well
The models come in Thinking and Instruct versions and utilize a new architecture, allowing it to have ~10x faster inference than Qwen32B. 💜 Step-by-step Guide: https://docs.unsloth.ai/models/qwen3-next
I run 20 AI coding agents locally on my desktop workstation at 400+ tokens/sec with MiniMax-M2. It’s a Sonnet drop-in replacement in my Cursor, Claude Code, Droid, Kilo and Cline peak at 11k tok/sec input and 433 tok/s output, can generate 1B+ tok/m.All with 196k context window. I'm running it for 6 days now with this config.
Today max performance was stable at 490.2 tokens/sec across 48 concurrent clients and MiniMax M2.
Running large language models efficiently is more than just raw GPU power. The latest guide breaks down the essential math to determine if your LLM workload is compute-bound or memory-bound.
We apply these principles to a real-world example: Qwen's 32B parameter model on the new NVIDIA RTX PRO 6000 Blackwell Edition.
In this guide, you will learn how to:
Calculate your GPU's operational intensity (Ops:Byte Ratio) Determine your model's arithmetic intensity Identify whether your workload is memory-bound or compute-bound
Join us on site October 22-23 to see how Arm empowers developers to build and deploy AI applications with ease using PyTorch and ExecuTorch. Learn about the latest AI technologies from Arm and our ecosystem while expanding your professional network alongside like-minded AI engineers.
a senior engineer at google just dropped a 400-page free book on docs for review: agentic design patterns.
the table of contents looks like everything you need to know about agents + code: > advanced prompt techniques > multi-agent patterns > tool use and MCP > you name it
This is what efficient AI looks like: Gemma 3n just dropped - a natively multimodal model that runs entirely on your device. No cloud. No API calls.
🧠 Text, image, audio, and video - handled locally. ⚡️Only needs 2B in GPU memory to run 🤯 First sub-10B model to hit 1300+ Elo ✅ Plug-and-play with Hugging Face, MLX, llama.cpp, and more.
Plus: Multilingual out of the box (140+ languages), fine-tune in a free Colab notebook.
