Search: “llm”

Large language models (LLMs) with integrated search tools show strong promise in open-domain question answering (QA), yet they often struggle to produce complete answer set to complex questions such a

Significant contributors to this article include Sneha Aradhey, Software Engineer, Google Kubernetes Engine, and Michael MacDonald, Sr Software Engineer, Google Cloud Managed Lustre. Enterprise produc

QIMMA (قِمّة) is a quality-focused leaderboard for evaluating Arabic Large Language Models, developed by TIIUAE. The leaderboard benchmarks Arabic LLMs across various performance metrics and tasks to

Running LLMs across Cloudflare’s network requires us to be smarter and more efficient about GPU memory bandwidth. That’s why we developed Unweight, a lossless inference-time compression system that ac

This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models (NADPFM) at ICLR 2026. Principled domain reweighting can substantially improve sample efficienc

Nvidia Corp. today announced the launch of customizable Nemotron 3.5 Super LV large language models for always-on artificial intelligence agents and edge-focused 3.5 Bolt for local systems. The compan

Apple paper: keep specs, schemas, tool config, and constraints; drop session traces. Selective shared memory beat both no memory and full history.

Benchmark two 30B Mixture-of-Experts models, Qwen3-Coder-30B and NVIDIA Nemotron-3-Nano-30B, across G5, G6, G6e, and G7 GPU instances on Amazon SageMaker AI. Compare throughput, latency, and cost-per-

Microsoft published a 109-page technical report on MAI-Thinking-1. Here’s the abbreviated version of how a modern lab actually trains a frontier reasoning model — from scraping the web to reinforcemen

OpenAI and Broadcom announced a collaboration on a specialized inference chip designed to optimize the performance and efficiency of large language model deployments. The chip, referred to as 'Jalapen

This resource compiles significant large language model research papers published between January and May 2026, curated by Sebastian Raschka. It serves as a reference guide for tracking recent advance

Most production AI features don't need a frontier model. Here's how capability evals and prompt engineering can help ship a local SLM that matches frontier-model quality with lower latency and cost. T

This article discusses recent advancements in large language model architecture design, focusing on key-value (KV) sharing techniques, multi-head cache (mHC) mechanisms, and compressed attention metho

This blog post discusses higher-order optimization algorithms such as Shampoo that have been applied in neural network training for at least a decade. The article explores how these emerging optimizer

The Amazon SageMaker Python SDK v3 now exposes generative AI inference recommendations in Amazon SageMaker AI directly in your notebook. Benchmark an endpoint, generate data-driven deployment recommen

Hank Green, a popular YouTuber and science communicator, said he is stepping back from production amid intense criticism over his use of AI. Green described his AI usage as 'not healthy,' but stressed

GPU utilization can read healthy while your queue backs up, and a new replica takes minutes to warm. Here's how to pick autoscaling metrics, tune scale-up/down windows, and budget for cold starts on d

AI model co-design involves integrating model requirements into hardware planning to ensure memory hierarchies and networking stacks are purpose-built for specific systems . AI performance balances th

Large language model training workloads increasingly encounter GPU memory limits before compute is fully utilized, with high-bandwidth memory capacity becoming the primary scaling bottleneck as model

If you’re iterating on deploying large language models (LLMs) on AWS GPU instances, you’ve probably noticed the larger the model to be loaded into GPU High Bandwidth Memory (HBM), the longer the painf

This article from Databricks discusses strategies and best practices for deploying large language models in production environments at scale while maintaining reliability and performance. It likely co

This article describes techniques for using prompt caching to improve the inference speed and efficiency of open-source large language models when deployed on Databricks' platform. Prompt caching redu

This article explains how retrieval quality directly impacts large language model performance in retrieval-augmented generation (RAG) systems, emphasizing that even advanced LLMs cannot produce better

This guide from Databricks covers practical techniques and best practices for fine-tuning large language models, including methodologies for adapting pre-trained LLMs to specific tasks and domains. It

Large Language Models (LLMs) demonstrate their reasoning ability through chain-of-thought (CoT) generation. However, LLM’s autoregressive decoding may limit the ability to revisit and refine earlier t

Running large language model inference at scale forces a KV cache trade-off: oversized GPU instances or slow time-to-first-token. This post builds a tiered KV cache on Amazon SageMaker HyperPod that e

It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conferen

Google updated Android Bench in July 2026 by adopting the Harbor framework and adding eight new models including Claude Fable 5, Claude Sonnet 5, and others to its LLM leaderboard for Android developm

This guide demonstrates how to deploy a vLLM (a fast, open-source LLM serving engine) server on Hugging Face Jobs using a single command, streamlining the process of setting up and running an inferenc

In this post, we introduce a systematic framework for LLM migration or upgrade in generative AI production, encompassing essential tools, methodologies, and best practices. The framework facilitates t

IBM has introduced **DocLang**, a new markup language specifically designed for large‑language models (LLMs). Developed by lead researcher Peter Staar and IBM’s document‑parsing team, DocLang aims to

LLMs have become more powerful at smaller sizes, but deploying them to edge devices like smartphones remains a massive challenge. Today, developers have to optimize across a sprawling combination of a

On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological exploration of everything t

Artificial intelligence platforms are adopting AI watermarking—specifically Google’s open‑source SynthID‑Text—to meet new EU regulations, embedding a secret key that subtly changes word selection and

A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector ada

Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. These startups are chasing the next big thing in LLMs Nine yea

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. A fundamental flaw leaves LLMs strikingly vulnerable to attack

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Ornith-1.0: Self-Scaffolding LLMs for Agentic Coding This is an interesting new open weights (MIT licensed) model, the first model release from DeepReinforce. [...] with variants including 9B Dense, 3

I put together these annotated slides from my five minute lightning talk at PyCon US 2026, using the latest iteration of my annotated presentation tool. # I presented this lightning talk at PyCon US 2

This article from Hugging Face discusses an approach to reinforcement learning (RL) that prioritizes ensuring model correctness as a foundational step before applying corrections or refinements. The p

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