Hao Li

Get faster and smarter MOE inference straight out of the box. 👇Deep dive on scaling expert parallelism with TensorRT-LLM. LLMs with MOE promise higher model capacity without linearly increasing compute costs - but they introduce new challenges -- more conditional computation, dynamic routing, and non-uniform GPU utilization -- solved by TensorRT-LLM. ✨ New in TensorRT-LLM has native support for expert parallelism—designed for fast, efficient inference with MoE models like Mixtral (Mistral AI) and DeepSeek (DeepSeek AI). This gives you: ✅ Dynamic expert routing: Automatically route tokens to the top-k experts with minimal overhead. ✅ Efficient expert scheduling: Balance expert loads across GPUs using smart sharding and token bucketization. ✅ Memory-aware execution: Maximize hardware utilization while respecting memory budgets. ✅ Drop-in support: Use @HuggingFace models with minimal code changes via TensorRT-LLM's #Python API. 🧠 How it works: MoE models activate only a subset of "experts" for each token. This dynamic nature is powerful—but hard to optimize. It’s all done under the hood using custom #CUDA kernels and NCCL-based communication primitives—giving you low latency, high throughput, and better GPU scaling. ✨ TensorRT-LLM handles: ✅Token-expert mapping using the gating network. ✅Token sorting to batch same-expert tokens together. ✅Expert parallel execution across GPUs. ✅Merging outputs for final predictions. 🛠️ Developer workflow - here is the code to get started. # Clone the repo git clone https://lnkd.in/g-GiDX23 # Use included examples to load and run a Mixtral model cd TensorRT-LLM/examples/mixtral From there, the Python API lets you load the model, convert it with TensorRT, and run expert parallel inference—all with a few lines of code. Results? 📈 Performance at scale. Tests show up to 2.3x faster inference throughput compared to standard tensor parallelism when using 8 GPUs and top-2 experts per token. Even better—TensorRT-LLM keeps efficiency high across increasing batch sizes. Want to see it in action or contribute? 👉 Read the full tech blog: https://lnkd.in/g_7YV3vV 👉 Explore the code on GitHub: https://lnkd.in/gNjQ5W2U 👉 Follow updates in the TensorRT-LLM repo: https://lnkd.in/gqSHYQ4u Share your experiences with us.

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We developed an open-source AI tool to design concrete mixes that are stronger, more sustainable, and ready to build with faster—speeding up construction while reducing environmental impact. https://lnkd.in/gPCk8tCM But the impact of this AI tool is not just hypothetical! Amrize used Meta’s AI-based technologies to design a new low-carbon mix, and successfully deployed it in an at-scale slab-on-grade application at Meta's new data center in Rosemont, MN. Compared to the legacy mix, this new AI-designed mix is: 🦁 Stronger ⏱ Faster 🍃 Lower carbon ⏱ ️The ideal set time All this was achieved without needing any new materials, nor special equipment. Best of all, the AI is open-sourced. https://lnkd.in/g2KA7KZW This work was featured in a Meta engineering blog article published today! https://lnkd.in/gBU9HY8H

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New short course: DSPy: Build and Optimize Agentic Apps DSPy is a powerful open-source framework for automatically tuning prompts for GenAI applications. In this course, you'll learn to use DSPy, together with MLflow. This is built in partnership with Databricks and taught by Chen Qian, co-lead of the DSPy framework. Many AI builders spend hours hand-tuning prompts. When given a set of evals, DSPy automates this process. It’s especially useful for optimizing prompts, including few-shot prompts, in complex agentic AI workflows. Further, if you switch an application to a newer LLM, performance can degrade if your prompts were optimized to the previous model. DSPy automatically optimizes the entire system for the new LLM as well, using just a few evaluation examples. This course teaches DSPy works, and best practices for using it. You’ll write programs using DSPy’s signature-based programming model, debug them with MLflow tracing -- to gain visibility into how different parts of a pipeline, as well as how the overall system, are performing -- and automatically improve their accuracy with DSPy Optimizer. Please sign up here: https://lnkd.in/gdjae8AX

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Took some time to refresh my knowledge on the latest large-scale recommendation modeling. Takeaways: • 📈 Scaling the classical deep-wide ranking models: Longer user sequences + richer feature interactions under tight latency. Bytedance’s Rankmixer and LONGER papers showcase masterful architecture design. • 🤖 Generative recommendations: Moving beyond next-token prediction to next-action prediction. 🔁 “Action Speaks Louder than Words” (Meta) — one model for both retrieval and ranking. 📄 “A Generative Re-ranking Model for List-level Multi-objective Optimization” (Taobao) — predicts engagement for an entire list, full-page optimization with some greedy tricks. 🎯 “Recommender Systems with Generative Retrieval” (Google) — a “true generative” approach using semantic IDs (powerful, though tricky for fresh content) to generate the content directly. The real bottleneck today isn’t model architecture anymore — it’s training and serving at scale. GPT-style pipelines (OpenAI, Gemini, Claude) are setting the benchmark, and RecSys engineers are rapidly adopting similar techniques. It won't surprising to see MMOE + post-training ideas to show up in generative recsys soon. Just as GPT lowered the barrier for NLP, transformers will lower the barrier for next-gen recommendation systems. As GPUs get cheaper and more powerful, definitely more companies will adopt them. 🚀

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Our latest PyTorch community blog from the SGLang Team covers how SGLang advances hybrid Mamba plus Attention models with improved memory efficiency, prefix caching, speculative decoding, and serving performance. 📖 Read the blog: https://lnkd.in/gfDZkZWF #PyTorch #AIInfrastructure #SGLang #LLM

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Inference at scale is evolving. The future is composable systems where offline batch, online batch (near-realtime), and realtime traffic can all be served from a single, unified inference endpoint for most use-cases. This simplifies your architecture, reduces operational overhead, and increases throughput. Our new tutorial series will show you how to build it step by sep. In the first installment, Erik Saarenvirta walks you through creating a scalable image classification system on GKE that can be adapted to a variety of use-cases. Leave us feedback in comments or ask questions we are happy to answer. Kent Hua Ishmeet Mehta Erik Saarenvirta https://lnkd.in/dVNiUhwE

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When your compute budget is limited, should you prioritize model parameters or training tokens? Shuyang Xiang's experiment with tiny transformers shows that for smaller budgets, smaller models trained on more data often perform better.

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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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Streamline your machine learning workflows with CI/CD in Julia. From model validation to deployment, discover how to build scalable, test-driven, and reproducible ML systems using Julia’s powerful ecosystem. https://lnkd.in/esWGw8MT #JuliaLang #MLOps #CI_CD #MachineLearning #programming

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black-forest-labs/FLUX.2-klein-9B is crazy fun! Under FLUX Non-Commercial License, it bring: - Real-time AI agents with visual reasoning. - Instant, iterative design workflows. - Low-latency production pipelines on consumer-grade hardware.  - Sub-second speed - Text-to-Image + Native Editing in one pipeline. - Fits in ~20GB VRAM (RTX 4090 ready). The 4-step distilled version is a game-changer for anyone looking to deploy high-quality vision models without the massive VRAM overhead. #GenerativeAI #MachineLearning #FLUX2 #OpenSource #AIEngineering

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