Month: June 2025

XRP Ledger’s Ethereum-Compatible Sidechain to Go Live in Q2

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Decentralized Layer 1 blockchain XRP Ledger’s (XRPL) sidechain that’s compatible with the Ethereum Virtual Machine (EVM) is set to go live in the second quarter, Jaazi Cooper, director of product management at Ripple, and David Schwartz, Ripple’s chief technology officer said at the ongoing APEX 2025 conference in Singapore. EVM compatibility refers to the ability … Read more

Ripple’s Brad Garlinghouse Says Circle IPO Signals U.S. Stablecoin Regulation Ahead

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SINGAPORE – Brad Garlinghouse, CEO of crypto company Ripple Labs, stated at the XRP Ledger Apex, the Ripple (XRP) community conference in Singapore, that he remains bullish on stablecoins – a sentiment he said is reinforced by the recent blockbuster Circle CRCL initial public offering (IPO). “Circle IPO’s clearly went very well. That’s a reflection … Read more

Large Language Models: Inference Process and KV-Cache Structure

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Table of Links Abstract and 1 Introduction 2 Background 2.1 Large Language Models 2.2 Fragmentation and PagedAttention 3 Issues with the PagedAttention Model and 3.1 Requires re-writing the attention kernel 3.2 Adds redundancy in the serving framework and 3.3 Performance Overhead 4 Insights into LLM Serving Systems 5 vAttention: System Design and 5.1 Design Overview … Read more

vAttention: Contiguous KV-Cache for Faster, Simpler LLM Inference

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Table of Links Abstract and 1 Introduction 2 Background 2.1 Large Language Models 2.2 Fragmentation and PagedAttention 3 Issues with the PagedAttention Model and 3.1 Requires re-writing the attention kernel 3.2 Adds redundancy in the serving framework and 3.3 Performance Overhead 4 Insights into LLM Serving Systems 5 vAttention: System Design and 5.1 Design Overview … Read more

LLM Training Hyperparameters: Detailed Overview

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Table of Links Abstract and 1. Introduction 2. Method 3. Experiments on real data 3.1. Benefits scale with model size and 3.2. Faster inference 3.3. Learning global patterns with multi-byte prediction and 3.4. Searching for the optimal n 3.5. Training for multiple epochs and 3.6. Finetuning multi-token predictors 3.7. Multi-token prediction on natural language 4. … Read more