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Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability, the KV cache footprint increases and becomes a major bottleneck for throughput and latency. For modern Transformers, this cache can occupy multiple gigabytes. NVIDIA researchers have introduced KVTC (KV Cache Transform Coding). This lightweight transform coder compresses KV caches for compact on-GPU and off-GPU storage. It achieves up to 20x compression while maintaining reasoning and long-context accuracy. For specific use cases, it can reach 40x or higher. https://ift.tt/5XGQN03 The Memory Dilemma in LLM Inference In production, inference frameworks treat local KV caches like databases. Strategies like prefix sharing promote the reuse of caches to speed up responses. However, stale caches consume scarce GPU memory. Developers currently face a difficult choice: Keep the cache: Occupies memory needed ...

Google Research is proposing a new way to build accessible software with Natively Adaptive Interfaces (NAI), an agentic framework where a multimodal AI agent becomes the primary user interface and adapts the application in real time to each user’s abilities and context. Instead of shipping a fixed UI and adding accessibility as a separate layer, NAI pushes accessibility into the core architecture. The agent observes, reasons, and then modifies the interface itself, moving from one-size-fits-all design to context-informed decisions. What Natively Adaptive Interfaces (NAI) Change in the Stack ? NAI starts from a simple premise: if an interface is mediated by a multimodal agent, accessibility can be handled by that agent instead of by static menus and settings. Key properties include: The multimodal AI agent is the primary UI surface. It can see text, images, and layouts, listen to speech, and output text, speech, or other modalities. Accessibility is integrated into this agent f...

In this tutorial, we walk through advanced usage of Einops to express complex tensor transformations in a clear, readable, and mathematically precise way. We demonstrate how rearrange, reduce, repeat, einsum, and pack/unpack let us reshape, aggregate, and combine tensors without relying on error-prone manual dimension handling. We focus on real deep-learning patterns, such as vision patchification, multi-head attention, and multimodal token mixing, and show how einops serves as a compact tensor manipulation language that integrates naturally with PyTorch. Check out the  FULL CODES here . Copy Code Copied Use a different Browser import sys, subprocess, textwrap, math, time def pip_install(pkg: str): subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", pkg]) pip_install("einops") pip_install("torch") import torch import torch.nn as nn import torch.nn.functional as F from einops import rearr...

Alibaba Tongyi Lab research team released ‘Zvec’, an open source, in-process vector database that targets edge and on-device retrieval workloads. It is positioned as ‘the SQLite of vector databases’ because it runs as a library inside your application and does not require any external service or daemon. It is designed for retrieval augmented generation (RAG), semantic search, and agent workloads that must run locally on laptops, mobile devices, or other constrained hardware/edge devices The core idea is simple. Many applications now need vector search and metadata filtering but do not want to run a separate vector database service. Traditional server style systems are heavy for desktop tools, mobile apps, or command line utilities. An embedded engine that behaves like SQLite but for embeddings fits this gap. https://ift.tt/65kCdQi Why embedded vector search matters for RAG ? RAG and semantic search pipelines need more than a bare index. They need vectors, scalar fields, full CR...

In this tutorial, we demonstrate how to federate fine-tuning of a large language model using LoRA without ever centralizing private text data. We simulate multiple organizations as virtual clients and show how each client adapts a shared base model locally while exchanging only lightweight LoRA adapter parameters. By combining Flower’s federated learning simulation engine with parameter-efficient fine-tuning, we demonstrate a practical, scalable approach for organizations that want to customize LLMs on sensitive data while preserving privacy and reducing communication and compute costs. Check out the  FULL CODES here . Copy Code Copied Use a different Browser !pip -q install -U "protobuf<5" "flwr[simulation]" transformers peft accelerate datasets sentencepiece import torch if torch.cuda.is_available(): !pip -q install -U bitsandbytes import os os.environ["RAY_DISABLE_USAGE_STATS"] = "1" os.environ["TOKENIZERS_PARALLELISM...

Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model training. Downlink bandwidth is the main bottleneck. Images can sit on orbit for days while ground models train on partial and delayed data. Microsoft Researchers introduced ‘OrbitalBrain’ framework as a different approach. Instead of using satellites only as sensors that relay data to Earth, it turns a nanosatellite constellation into a distributed training system. Models are trained, aggregated, and updated directly in space, using onboard compute, inter-satellite links, and predictive scheduling of power and bandwidth. https://ift.tt/7fWK2vp The BentPipe Bottleneck Most commercial constellations use the BentPipe model. Satellites collect images, store them locally, and dump them to ground stations whenever they pass overhead. The research team evaluates a Planet-like constellation with 207 satellites and 12 ground station...