VentureBeat

Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...

Jama Software Launches Model Context Protocol (MCP) Server

Increases Product Velocity via AI-Driven Development for Regulated, Multidisciplinary ProductsPortland, OR, May 04, 2026 (GLOBE NEWSWIRE) -- Jama Software®, the leader in intelligent engineering ...
VentureBeat

MIT’s new ‘recursive’ framework lets LLMs process 10 million tokens without context rot

Recursive language models (RLMs) are an inference technique developed by researchers at MIT CSAIL that treat long prompts as an external environment to the model. Instead of forcing the entire prompt ...
Semiconductor Engineering

Efficient LLM Inference With Limited Memory (Apple)

A technical paper titled “LLM in a flash: Efficient Large Language Model Inference with Limited Memory” was published by researchers at Apple. “Large language models (LLMs) are central to modern ...
Semiconductor Engineering

AI Inference Needs A Mix-And-Match Memory Strategy

Interactive LLMs (chat, copilots, agents) with strict latency targets Long‑context reasoning (codebases, research, video) with massive KV (key value) cache footprints Ranking and recommendation models ...
InfoQ

Cactus v1: Cross-Platform LLM Inference on Mobile with Zero Latency and Full Privacy

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
InfoWorld

Google targets AI inference bottlenecks with TurboQuant

Google says its new TurboQuant method could improve how efficiently AI models run by compressing the key-value cache used in LLM inference and supporting more efficient vector search. In tests on ...
SDxCentral

AI inference crisis: Google engineers on why network latency and memory trump compute

Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...
GIGAZINE

'mesh-llm' allows you to locally run massive AI models by gathering resources from multiple PCs.

Mesh LLM is a mechanism that brings together the surplus GPU computing resources of multiple computers to enable distributed execution of large-scale language models that would be difficult to run on ...