Sygaldry Technologies just closed a $139 million funding round for quantum-accelerated AI servers, and everyone’s celebrating the money instead of […]
Database Design for Bots: Get It Right or Suffer Later Here’s the deal: I once built a scheduling bot that
Top 5 Mistakes When Using Fly.io for App Hosting I’ve seen 3 production agent deployments fail this month. All 3
100 billion parameters. That’s a staggering number in the world of large language models (LLMs). Until recently, training models of
100 billion parameters. That’s the staggering model size MegaTrain, a new system announced in April 2026, claims to handle on
Cracking the LLM Memory Wall One hundred billion parameters. On a single GPU. Full precision. When I first saw the
The Billion-Parameter Bottleneck 100 billion parameters. That’s the staggering number MegaTrain targets. For a backend engineer like me, working with
1.84 times the training throughput of DeepSpeed ZeRO-3 when working with 14B models. That’s a significant jump for anyone pushing
1.84 times faster. That’s the throughput improvement MegaTrain claims over DeepSpeed ZeRO-3 when working with 14B models. As a backend
100 billion parameters. That’s the astonishing number we’re talking about for a single GPU, training large language models (LLMs) at