Guides, benchmarks, news, and insights on GPU computing, AI inference, and cloud infrastructure.
Pearl (PRL) replaces wasteful hashing with AI matrix math. What Pearl Research is, which GPUs mine it, and how to rent compute on Paralon to start.
Shipping software over the internet to a decentralized fleet, explained as a lesson: why you ask instead of push, the three gates every update passes, and how the network decides it can spare a node before letting one leave to upgrade.
What we learned running a 27B coding model across a heterogeneous fleet of consumer GPUs — why the obvious sizing formula silently OOMs, why pipeline parallelism behaves nothing like the benchmarks suggest, and the gotchas nobody writes down.
AI workloads handle sensitive data — and most enterprises can't afford to send prompts through someone else's servers. Here's how Paralon Enterprise delivers production-grade inference without leaving your network.
Paralon Cloud announces a strategic academic partnership with the Romanian-American University, giving students and researchers access to our OpenAI-compatible inference API for AI projects, research, and education.
We built Paralon Cloud to make GPU compute accessible to anyone — developers, researchers, and creators. Here's what we offer, why we built it, and where we're heading.
The Paralon Cloud resource center is your comprehensive guide to GPU cloud computing, AI inference, and decentralized infrastructure. Find tutorials on deploying LLMs like Llama 3, Mixtral, and Qwen on consumer and enterprise GPUs. Compare GPU performance with our in-depth benchmarks of RTX 4090, A100, and H100. Stay updated with the latest industry news and platform updates.
