hackintoshrao

Scaling Iceberg Writes with Confidence: A Conflict-Free Distributed Architecture for Fast, Concurrent, Consistent Append-Only Writes

Discover how compiler-generated futures work behind the scenes—and why they’re both powerful and perplexing.

I just published a blog exploring "Is Apache Ray the Ideal Framework for Distributed LLM Training and Inference?"

Given that reinforcement learning (RL) requirements heavily influenced Apache Ray's architecture, could this focus hinder optimal performance for large language model (LLM) workloads? I've shared some thoughts and would love to hear your insights.

I'm particularly interested in feedback from the Apache Ray and Anyscale communities and anyone working with LLMs and distributed computing.