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Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference

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Liquid AI just released LFM2.5-VL-450M, an updated version of its earlier LFM2-VL-450M vision-language model. The new release introduces bounding box prediction, improved instruction following, expanded multilingual understanding, and function calling support — all within a 450M-parameter footprint designed to run directly on edge hardware ranging from embedded AI modules like NVIDIA Jetson Orin, to mini-PC APUs like AMD Ryzen AI Max+ 395, to flagship phone SoCs like the Snapdragon 8 Elite inside the Samsung S25 Ultra. What is a Vision-Language Model and Why Model Size Matters Before going deeper, it helps to understand what a vision-language model (VLM) is. A VLM is a model that can process both images and text together — you can send it a photo and ask questions about it in natural language, and it will respond. Most large VLMs require substantial GPU memory and cloud infrastructure to run. That’s a problem for real-world deployment scenarios like warehouse robots, smart glasses...
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How Knowledge Distillation Compresses Ensemble Intelligence into a Single Deployable AI Model

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Complex prediction problems often lead to ensembles because combining multiple models improves accuracy by reducing variance and capturing diverse patterns. However, these ensembles are impractical in production due to latency constraints and operational complexity. Instead of discarding them, Knowledge Distillation offers a smarter approach: keep the ensemble as a teacher and train a smaller student model using its soft probability outputs. This allows the student to inherit much of the ensemble’s performance while being lightweight and fast enough for deployment. In this article, we build this pipeline from scratch — training a 12-model teacher ensemble, generating soft targets with temperature scaling, and distilling it into a student that recovers 53.8% of the ensemble’s accuracy edge at 160× the compression. What is Knowledge Distillation? Knowledge distillation is a model compression technique in which a large, pre-trained “teacher” model transfers its learned behavior to...
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Alibaba’s Tongyi Lab Releases VimRAG: a Multimodal RAG Framework that Uses a Memory Graph to Navigate Massive Visual Contexts

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Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and start mixing in images and videos, the whole approach starts to buckle. Visual data is token-heavy, semantically sparse relative to a specific query, and grows unwieldy fast during multi-step reasoning. Researchers at Tongyi Lab, Alibaba Group introduced ‘VimRAG’, a framework built specifically to address that breakdown. The problem: linear history and compressed memory both fail with visual data Most RAG agents today follow a Thought-Action-Observation loop — sometimes called ReAct — where the agent appends its full interaction history into a single growing context. Formally, at step t the history is H t = [q, τ 1 , a 1 , o 1 , …, τ t-1 , a t-1 , o t-1 ]. For tasks pulling in videos or visually rich documents, this quickly becomes untenable: the information density of critical observations |O crit |/|H t | fal...
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NVIDIA Releases AITune: An Open-Source Inference Toolkit That Automatically Finds the Fastest Inference Backend for Any PyTorch Model

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Deploying a deep learning model into production has always involved a painful gap between the model a researcher trains and the model that actually runs efficiently at scale. TensorRT exists, Torch-TensorRT exists, TorchAO exists — but wiring them together, deciding which backend to use for which layer, and validating that the tuned model still produces correct outputs has historically meant substantial custom engineering work. NVIDIA AI team is now open-sourcing a toolkit designed to collapse that effort into a single Python API. NVIDIA AITune is an inference toolkit designed for tuning and deploying deep learning models with a focus on NVIDIA GPUs. Available under the Apache 2.0 license and installable via PyPI, the project targets teams that want automated inference optimization without rewriting their existing PyTorch pipelines from scratch. It covers TensorRT, Torch Inductor, TorchAO, and more, benchmarks all of them on your model and hardware, and picks the winner — no guessing...
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An End-to-End Coding Guide to NVIDIA KVPress for Long-Context LLM Inference, KV Cache Compression, and Memory-Efficient Generation

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In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context language model inference more efficient. We begin by setting up the full environment, installing the required libraries, loading a compact Instruct model, and preparing a simple workflow that runs in Colab while still demonstrating the real value of KV cache compression. As we move through implementation, we create a synthetic long-context corpus, define targeted extraction questions, and run multiple inference experiments to directly compare standard generation with different KVPress strategies. At the end of the tutorial, we will have built a stronger intuition for how long-context optimization works in practice, how different press methods affect performance, and how this kind of workflow can be adapted for real-world retrieval, document analysis, and memory-sensitive LLM applications. Copy Code Copied Use a different Browser import os, sy...
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