The Next Phase Of Enterprise AI: Why LLM Consolidation Is Inevitable

According to a 2025 survey by Global Market Insights, the top five providers—Anthropic, AWS, Google, Microsoft and ...
InfoWorld

21 LLMs tuned for special domains

Large language models are not just getting smarter, they’re becoming more specialized. Turn to these models for deep ...
TestingCatalog

OpenSquilla launches open-source AI agent to cut token costs

OpenSquilla is an open-source Python AI agent with ML model routing, four-tier memory, and syscall-level sandbox isolation.
Microsoft

Online Scheduling for LLM Inference with KV Cache Constraints

Large Language Model (LLM) inference, where a trained model generates text one word at a time in response to user prompts, is a computationally intensive process requiring efficient scheduling to ...
marktechpost

An End-to-End Coding Guide to NVIDIA KVPress for Long-Context LLM Inference, KV Cache Compression, and Memory-Efficient Generation

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 ...
GitHub

Ollama vs Atomic Chat (TurboQuant KV Cache)

GPU memory is THE story. Ollama uses 13-19GB of unified memory during inference vs Atomic Chat's constant ~5GB. TurboQuant's 3-bit KV cache compression delivers its promised ~3.5x memory reduction.
winbuzzer.com

Google’s TurboQuant Algorithm Slashes LLM Memory Use by 6x

Running a 70-billion-parameter large language model for 512 concurrent users can consume 512 GB of cache memory alone, nearly four times the memory needed for the model weights themselves. Google on ...
Microsoft

KEEP: A KV-Cache-Centric Memory Management System for Efficient Embodied Planning

Memory-augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long-horizon embodied planning. By keeping track of past experiences and environmental states, ...
VentureBeat

Nvidia says it can shrink LLM memory 20x without changing model weights

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...
ZDNet

How to clear your MacBook cache (and why it'll do wonders for performance)

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Best laptop cooling pads Best flip ...
IEEE

ELORA: Efficient LoRA and KV Cache Management for Multi-LoRA LLM Serving

Abstract: Multiple Low-Rank Adapters (Multi-LoRA) are gaining popularity for task-specific Large Language Model (LLM) applications. For Multi-LoRA serving, caching hot LoRAs and KV caches in the GPU ...
Bloomberg L.P.

Why the AI Boom Will Make Phones, Cars and Electronics More Expensive

AI demand is triggering a historic memory-chip shortage. Meeting exponential demand for chips will be expensive and maybe even impossible. To secure capacity for AI systems, tech giants are buying up ...