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Guides, benchmarks, news, and insights on GPU computing, AI inference, and cloud infrastructure.
Inference on ParalonCloud is now paid, and it settles one request at a time: the moment a response completes, 80% of what it cost lands in the earnings of whoever owns the GPU that served it. No monthly statement, no batch. Here is how a request turns into money, why the cards we want most are the RTX 5090 and the RTX PRO 6000, and what changes if you call the API.
A mixed GPU fleet has one setting per model, and it has to be safe on the slowest machine in it. We measured what that costs: an RTX 5090 running at 8% of its memory bandwidth because a 24GB card three racks away needed a flag. Two arguments, applied only to the cards that could take them, made it fifteen times faster. Here is what the engine measures per node, what it changes, and the two things that surprised us.
Connect a graphics card to ParalonCloud and it earns on two independent tracks: real USDC whenever someone rents it, and Season 0 points for every minute it stays online, whether anyone rents it or not. Here is how the split works, what each card is worth per hour, and why the cards we want most are the ones with the most VRAM.
Ask any AI provider which machine processed your prompt and which jurisdiction it sat in, and you will not get an answer. A decentralized network is the only architecture that could answer it — so we went looking at what ours can actually prove today. The honest version: less than we would like, and more than anyone else offers.
Providers run GPUs on home connections behind NAT — no static IP, no port forwarding, no inbound anything. Renters still get an HTTPS address that works the moment the container starts and stops existing when the session ends. How we build and destroy one per rental, and the three constraints that shaped the design.
Two RTX 4090s on our network, same container, same model, same arguments. One runs Windows with WSL2 and benchmarks better on every hardware axis we measure. It serves inference 48% slower — three times the penalty the published figures report. Here is the measurement, what we ruled out, and why we think large quantized models are the worst case for GPU virtualisation.
Every node has reported its connection speed since January. We published a 100 Mbps requirement in June. Nothing connected the two, so in August five of the nodes we were offering to renters were below it, one at 15 Mbps. What we found, why a single speed test can't be the gate, and why we kept those nodes on the network anyway.
Anyone can claim an H100. On a network built from other people's hardware, a claim is worth nothing — so we measure instead. How ParalonCloud verifies every GPU by benchmarking it, what the real numbers look like across 80 verified nodes, and what the method still can't catch.
How ParalonCloud runs LLMs, vision, image generation, speech, rendering and GPU rentals on one distributed network of consumer GPUs — with a single agent, a VRAM-aware scheduler, and OpenAI-compatible gateways. The architecture, the numbers, and what it makes possible for partners.
Our inference API now generates images. Z-Image-Turbo — open-weight, Apache 2.0 — is live behind an OpenAI-compatible /v1/images/generations endpoint, running on consumer GPUs in our network. Free during beta. Try it from curl, Python, or straight from our Discord.
Our inference API can now see. Qwen 3.8 27B — natively multimodal, 32k context, tool calling — is live on the ParalonCloud API, free during beta. Send screenshots and images through the same OpenAI-compatible endpoint you already use.
You can now rent a GPU without leaving your editor. The ParalonCloud Rentals MCP server lets Claude Code, Claude Desktop, and Cursor browse GPUs, start a rental, and hand you the connection URL — in natural language, with one config line.
You can now rent GPUs on ParalonCloud from code. Browse available GPUs with live prices, start a JupyterLab rental, get its connection URL, and stop it — all over a simple REST API, with the same key you use for inference.
A host-side comparison of ParalonCloud and Vast.ai. What you actually keep, how and when you get paid, KYC, setup friction, and where each marketplace wins — for anyone renting out an idle NVIDIA GPU in 2026.
Cycles frames lock up your machine for minutes; animations take it hostage for hours. ParalonCloud Render is a cloud Blender render farm on a decentralized network of NVIDIA RTX GPUs — upload a .blend, render with Cycles or Eevee, download your frames. 4K/8K, animations, free during beta.
OpenClaw is free — the model behind it usually isn't. Here's the complete openclaw.json custom-provider setup to run OpenClaw on ParalonCloud's free OpenAI-compatible inference API, with Qwen 3.6 27B doing the thinking and an honest guide to living inside a 32k context window.
pi is an open-source coding agent in the spirit of Claude Code — except you choose the model behind it. Here's the full setup to point pi at any OpenAI-compatible endpoint, using ParalonCloud's inference API and Qwen 3.6 27B as the worked example.
Providers on ParalonCloud can now set their own hourly price, per node. Here's how custom pricing works, what a 4090 or H100 actually earns, and how to pick a price that wins renters in a volatile GPU market.
Season 0 of the ParalonCloud incentivized testnet is live. Keep a GPU online and earn points — VRAM × time — climb the leaderboard, and get recognized as an early provider on the network.
Turn an idle NVIDIA GPU into income on Paralon. Per-minute USDC/USDT payouts on Solana, no KYC, 80% provider share. Consumer cards like the RTX 4090 and RTX 5090 to data-center A100, H100 and H200.
ParalonCloud's inference API has no subscription, no seats, no monthly minimum and no auto-renewal. You add credits by card or crypto, every request is billed the moment it completes, to the token, and an invoice lands in your inbox. Here is the whole billing model on one page, including the parts that are not perfect yet.
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.
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The ParalonCloud 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.