GPU cloud guides and benchmarks

Oct 22, 20267 min read

The Provider API: Run Your GPU Fleet on Paralon From Your Own Tools

7 calls

nodes, one node, add, install command, change, reclaim, earnings

~120 ms

median for a read, measured from a server in Germany

Oct 21, 202610 min read

Organizations on Paralon: One GPU Budget, Shared API Keys and a Role for Everyone on the Team

5 roles

Owner, Admin, Developer, Billing, Viewer, from one permission registry

1 balance

for the team's rentals, render jobs and API calls

Oct 19, 20264 min read

Checkpoint Your Training So a Spot GPU Can't Cost You Work

A spot GPU can be reclaimed with two minutes' notice and its disk is wiped when the rental ends. Here's a PyTorch training loop that checkpoints off the machine and reacts to the notice, so a reclaim costs minutes, not the run.

Oct 17, 20263 min read

Spot vs On-Demand GPUs: When the Cheaper One Makes Sense

A spot GPU costs less because its owner can take it back. Here's how to tell which of your jobs belong on spot, which need on-demand, and how to rent each on ParalonCloud.

Oct 15, 20265 min read

How We Take a GPU Back in Two Minutes Without Dropping a Request

Spot nodes promise their owners the machine back within two minutes. Doing that without cutting off inference requests mid-answer took a draining state, a fix to a heartbeat that kept undoing it, and one switch that the whole platform obeys.

Oct 15, 20268 min read

Merge-Mine Pearl (PRL) and NACRE (NACR) on One Rented GPU with AlphaPool: Setup, Script and the Catches

AlphaPool lets one Pearl miner earn a second coin, NACRE, from the same work. We ran it with alpha-miner on rented GPUs and wrote a script that downloads the latest miner, checks it and starts it on your addresses. Here is the setup, the pool parameters, the 12 GB memory floor, the math and the catches.

Oct 14, 20266 min read

Reserved GPU Capacity, With or Without a Down Payment: Paralon as the Layer Between Owner and Buyer

Owner's terms

a down payment and a minimum term, or neither: the owner decides

1 buyer

can rent capacity reserved for them; nobody else sees it

Oct 13, 20263 min read

Design and VFX Studios: Let Your Workstations Earn at Night

Studio and office workstations carry serious GPUs and work office hours. Here's how to run them as spot nodes on ParalonCloud overnight and at weekends, and have every one back by morning.

Oct 13, 20269 min read

Claude Code on an Open Model, No Router: the Anthropic Messages API on Qwen3.8 27B, Measured

7 lines

of environment variables, and no router

9 of 9

requests of a session answered, once all five model variables were set

Oct 12, 20267 min read

Two RTX 5090s on a 27B Model: 150 tok/s Alone, 1,300 at 32, Next to Four Other Cards

149–156 tok/s

one request, two cards

1,318 tok/s

in total at 32 concurrent requests

Oct 12, 20267 min read

Add an Open Model to 9Router as a Custom Provider: Setup and What We Measured

9Router puts one local endpoint in front of your coding tools and many model providers. Here is how to add an OpenAI-compatible API to it as a custom provider, with ParalonCloud and Qwen3.8 27B as the worked example, and what happened when Claude Code and opencode fixed a bug through it.

Oct 12, 20268 min read

Qwen3.8-27B on an H200: 3,300 tok/s at 128 Requests, After One Flag Stopped It Starting

163–176 tok/s

one request

3,342 tok/s

in total at 128 concurrent requests

Oct 11, 20267 min read

Our Benchmark Read an H200 at 48% of Spec. The Benchmark Was Wrong.

An NVIDIA H200 joined the network and our verification put its memory bandwidth at 2,319 GB/s, under half of the 4,800 on the spec sheet. The rent page warned renters about the card. We rented it ourselves, measured it another way, and found 4,110 GB/s. Here is how a bandwidth test that was right on every consumer card went wrong on a fast one, and the fix.

Oct 11, 20264 min read

Sell Spare Data-Center GPU Capacity Without Losing Control

GPU clusters are sized for peaks. Here's how data centers and companies with their own GPUs can put the capacity between peaks to work on ParalonCloud, and take it back in two minutes when internal workloads need it.

Oct 9, 20264 min read

Gaming Cafe After Hours: Put Idle PCs to Work, Take Them Back Before Opening

A gaming cafe's PCs are idle every night. Here's how to run them as spot nodes on ParalonCloud, and pause the whole floor, or one row, with a single click before customers arrive.

Oct 7, 20264 min read

Rent Out Your Gaming PC's GPU When You're Not Playing

Your gaming GPU sits idle most of the day. Here's how to rent it out on ParalonCloud while you're at work or asleep, and get it back in two minutes when you want to play.

Oct 6, 20266 min read

I Ran GPUs for Networks That Never Paid. So We Built One That Does.

Before Paralon I was a provider. I kept machines online for years in well-funded compute networks whose testnets never ended and never paid. This is what that taught me, the one rule we built Paralon on, and why our points come on top of payouts, not instead of them.

Oct 5, 20264 min read

Spot Nodes: Rent Out a GPU You Also Use, and Take It Back Anytime

ParalonCloud now has spot nodes: GPUs that earn while they're idle and go back to their owner the moment they're needed. What they are, how they work, and who they're for.

Sep 30, 20269 min read

Our Load Balancer Gave the Fastest GPU the Least Traffic

A new RTX PRO 6000 joined our network and got a quarter of the traffic of a pair of RTX 4090s it outperforms 3x under load. The GPU-name table had no entry for it, and our measured correction could not see the difference, because per-request speed is nearly equal on the two. What we replaced both with, the cold-start mistake we made on the way, and two routing bugs the new weights exposed.

Sep 29, 20267 min read

How Much VRAM Qwen3.8-27B Needs for Its Full 256k Context

Qwen3.8-27B is trained on 262,144 tokens of context, and a full-length request costs only about 9.3 GiB of KV cache in vLLM, because three layers in four keep no per-token cache at all. Here is the arithmetic, what we measured on a 96 GB card and a pair of 24 GB cards, a table from 24 to 96 GB, and why we would not stretch it to 1M with YaRN.

Sep 28, 20268 min read

RTX PRO 6000 vs Two RTX 4090s on a 27B Model: Equal on One Request, 3x Apart at 32

106 vs 94

tok/s on one request, RTX PRO 6000 vs two RTX 4090s

1,342 vs 435

tok/s in total at 32 concurrent requests

Sep 24, 20265 min read

vLLM: "There is no module or parameter named 'visual' in Qwen3_5Model" — loading a Qwen3.5 classification head

Serving OpenJev (Qwen3_5ForSequenceClassification) on vLLM fails at weight loading because the checkpoint carries the vision tower and vLLM's fallback picks the text-only class. The fix is a text-only copy of the checkpoint. Also: 'UVA is not available' on WSL2, and why vllm serve cannot take a Hugging Face subfolder.

Sep 23, 20267 min read

Catch RAG Hallucinations Sentence by Sentence with an NLI Classifier: 74 Planted Errors, 93% Caught, $0.013

A RAG pipeline on one API key: embeddings for retrieval, a 27B chat model for the answer, and OpenJev over /v1/classify to check every sentence of the answer against the passages it came from. We planted 74 wrong sentences in 148 answers so recall is exact: 93.2% caught at a 0.7 threshold, 4.6% of clean sentences flagged, 49 seconds and 1.3 cents for 463 sentences. The 27B used as a judge caught the same 69 for 2.4× the money and 3× the time. Script included.

Sep 22, 20268 min read

OpenJev vs an LLM Judge on 990 Planted Errors: Recall, Calibration, Cost

The same 990 support tickets with 495 deliberately wrong labels, judged two ways: OpenJev over /v1/classify and Qwen 3.8 27B as an LLM judge. Asked one pair at a time OpenJev catches 82.6% of the planted errors; asked to pick among all twelve intents it catches 98.2%, rejects the same 33 mislabelled 'complaint' rows the 27B judge rejected, and its probabilities are calibrated to within 3 points. Every number is exact because we planted the errors.

Sep 21, 20268 min read

OpenJev Is Live on an OpenAI-Compatible API: an Open Jev Alternative You Can Call Today

TypeSafe's Jev is hosted-only and behind a waitlist. OpenJev, the open NLI cross-encoder built on Qwen3.5, is now served on ParalonCloud behind POST /v1/classify: a label and calibrated probabilities per input, 3 ms per item in a batch, no generated text. Measured against a 27B LLM judge on the same pairs, with the vLLM recipe so you can run it yourself.

Sep 19, 202611 min read

Using n8n with an Open LLM API: One Credential Field, One Toggle, and the Node That Will Not Work

n8n talks to any OpenAI-compatible endpoint through the Base URL field of its OpenAI credential, and its OpenAI Chat Model node lists whatever /v1/models returns. n8n's OpenAI nodes now default to the Responses API, which most open endpoints answer with a 404; ours serves it, and both the Chat Model and the OpenAI node's Message a Model work. The setup, tested on n8n 2.38, a ticket-triage workflow you can import, and the one Extra Body line that cut the model's answer from 3.3 s to 1.0 s.

Sep 19, 20266 min read

"Stream ended without finish_reason": What an OpenAI-Compatible API Should Do When the Worker Dies Mid-Stream

A customer's coding agent showed us a screenshot: Error: Stream ended without finish_reason. On our side the request row said completed. Both were wrong in their own way. Here is what happens to a streamed completion when the GPU worker's connection drops, why the common gateway behaviour turns it into a silent truncation, and the three rules we now follow: retry before the first token, an explicit error event after it, and never count a cut stream as a success.

Sep 19, 20267 min read

Who Owns Uptime on a Rented GPU

On a marketplace built from other people's machines, uptime is two jobs, not one. The platform keeps itself always-on with rolling, zero-downtime deploys; the provider keeps the GPU alive for the length of a rental. Here's where the line sits — and how reputation holds each side to it.

Sep 17, 202611 min read

Own GPU vs Pay-per-Token: Where an RTX 4090 Breaks Even Against an LLM API

A measured cost model for running a 27B model on a card you own versus paying per token: what a 4090 actually produces at one and at sixteen concurrent requests, what it draws at the wall loaded and idle, and the utilization at which owning becomes cheaper. Sixteen requests deep, the card breaks even at about 20% utilization in Germany and 15% in the US. One request at a time, it never does. A calculator script takes your own numbers.

Sep 16, 20268 min read

Batch API for Open Models: Half-Price Chat, Embeddings and Responses Jobs with the OpenAI SDK

Paralon's Batch API takes a JSONL file of requests and returns a file of results, at 50% of the token price, through the same /v1/files and /v1/batches calls as OpenAI's. We ran it with the official Python SDK: 300 support tickets classified with zero failures, 100 embeddings and 20 Responses API drafts, billed at exactly half the live price, while live requests kept their normal latency.

Sep 16, 202615 min read

Route to the GPU That Already Has Your KV Cache

A follow-up question costs 0.54 seconds on the worker that answered the first one and 2.56 seconds on any other — the whole prompt prefilled again. We measured that across a live fleet, built cache-aware routing for two router replicas that must agree, and then found why our hybrid 27B showed a 0% hit rate: vLLM leaves prefix caching off on Mamba-style models unless you pass two arguments. With them, a repeated 13,000-token prompt goes from 7.2 s to 1.6 s — and a third flag brings the same win down to the 1,200-token chats where most traffic lives.

Sep 16, 202613 min read

LLM-as-a-Judge with an Open Model: 990 Planted Errors, 99.6% Caught, and What the Rubric Changes

We used Qwen 3.8 27B over an OpenAI-compatible API as a judge of dataset labels, and scored it against errors we planted ourselves so every number is exact. With a written rubric the judge caught 491 of 493 wrong labels and also rejected 58 labels the dataset called correct, 43 of them the same systematic mistake in the generator. Without the rubric it agreed with the dataset more and missed 20 planted errors. The rubric is not a tuning knob; it decides what the judge measures.

Sep 16, 20264 min read

Cloudflare Error 1010: Your API Is Returning 403 to Python Clients and Nothing in Your Logs Shows It

Our OpenAI-compatible API answered curl, Node, python-requests and every SDK with 200, and Python's urllib with a 403 that never reached our servers. The cause was Cloudflare's Browser Integrity Check, which is right for web pages and wrong for an API. How to recognise error code 1010, how to prove it is the edge and not your app, and the Configuration Rule that turns the check off for /v1/ only.

Sep 15, 202611 min read

The OpenAI Responses API on Open Models: responses.create, the Agents SDK and n8n, Measured on Qwen 3.8

Tools are moving from Chat Completions to OpenAI's Responses API, and most open-model endpoints answer it with a 404. We now serve /v1/responses on Qwen 3.8 27B and ran the official OpenAI SDK, the OpenAI Agents SDK and n8n against it unchanged: all 10 SDK checks behaved as designed, a 24-task tool-using agent correct 24 of 24 through all three paths, and no measurable latency added by the translation. What is not supported, and the one setting that cut an n8n node from 3.3 s to 1.0 s.

Sep 15, 202613 min read

Document Classification with an Open LLM API: 7,218 Documents, No Training Data, $0.05 per Thousand

We classified the full 20 Newsgroups test split with Qwen 3.8 27B over an OpenAI-compatible API, zero-shot, ten documents per request, with the label set enforced by a JSON schema. 72% accuracy on twenty classes with no labeled data, 70,000 documents an hour on one key, and a third of all errors inside three categories that overlap by definition. Few-shot examples doubled the cost and moved accuracy half a point.

Sep 14, 202610 min read

Invoice Data Extraction with a Vision LLM API: 60 Invoices, Every Field Scored, $0.78 per Thousand

We ran 60 invoices, half of them degraded like phone scans, through Qwen 3.8 27B's vision input over an OpenAI-compatible API with a JSON schema on the output. Vendor, customer, invoice number, currency, every amount and every line item came back right on all 60. The one thing that failed, half the time, was a date written with slashes, and the fix is two lines of code, not a bigger model.

Sep 14, 202612 min read

Semantic Search with an Open Embeddings API: 990 Tickets, 5 Languages, and Where Keyword Search Still Wins

We indexed 990 support tickets with a 384-dimension open embedding model over an OpenAI-compatible /v1/embeddings endpoint and searched them 1,290 ways, scored against known intents and a TF-IDF keyword baseline. Same-language, keyword search was better. Across languages it collapsed to chance while embeddings held 47 to 58% precision. Adding a 27B reranker over the top ten lifted every language by 17 to 37 points for $0.09 per thousand queries. The whole index cost a tenth of a cent.

Sep 13, 202611 min read

Synthetic Dataset Generation with an Open LLM: 8,600 Labeled Examples an Hour on an OpenAI-Compatible API

We generated 990 labeled customer-support examples with Qwen 3.8 27B through a plain OpenAI-compatible endpoint, schema-enforced with a forced tool call. Here is the script, the throughput and latency we measured, the token cost per thousand examples at list price, what the model got right, and the two ways it quietly repeats itself.

Sep 13, 20264 min read

"zsh: killed" on macOS After Re-downloading a Binary: the Code Signature Cache, and the One-Line Fix

A provider re-ran our Mac install command and every launch of the agent died instantly with "zsh: killed". The binary was fine, the same file ran on other Macs, and there was no crash log. The cause is how macOS caches code signatures per file: curl -o over an existing executable leaves one the kernel refuses to start. Here is how to recognise it, why it happens, and the fix for your install scripts.

Sep 10, 20268 min read

How to Mine Quantus (QTC) on a Rented GPU: Pool Setup, Real RTX 4090 Hashrate, and What It Costs

Quantus, a post-quantum proof-of-work chain, launched its mainnet on 9 September 2026 and is mineable on ordinary NVIDIA GPUs from day one. We rented an RTX 4090 on Paralon, pointed the pool's CUDA miner at quanpool, and measured 1.17 GH/s. Here is the complete setup, the pool parameters, the fees, the network numbers, and the math you should do before renting a second card.

Sep 10, 20266 min read

Run Cline on Your Own LLM API: OpenAI-Compatible Setup for VS Code, JetBrains and the CLI

Cline is one of the most-installed open-source coding agents for VS Code, and it lets you bring your own model. Here is the complete setup to point it at any OpenAI-compatible endpoint, using ParalonCloud's inference API and Qwen 3.8 27B as the worked example: the extension, the Plan/Act modes, the CLI, what to put in every field of the model configuration, and what a session actually costs.

Sep 10, 20266 min read

Rent a GPU From Inside Cline: the ParalonCloud MCP Server in VS Code

Cline can now rent an NVIDIA GPU for you, wait for it to come up, hand you the JupyterLab link and stop it when you are done, without leaving the editor. Here is the complete setup of the @paraloncloud/mcp MCP server in Cline, the six tools it adds, a real session, what keeps an agent from spending your credits by accident, and what it costs per minute.

Sep 9, 202610 min read

One Model Name, Three Engines: vLLM on Linux, vLLM on WSL and llama.cpp on Apple Silicon Behind One Endpoint

The same model ID is now served by vLLM on Linux GPUs, vLLM on Windows/WSL, and llama.cpp on Apple Silicon Macs, and the caller cannot tell which one answered. Here is what had to be made equal for that to be true, the tokens-per-second we measured on an M1 Ultra and an M4 Max next to the GPUs, the 27B experiment that failed first and the rules it left behind, and a self-update bug that only exists when your inference server is a child process.

Sep 9, 20268 min read

Two RTX 4090s Over PCIe: Splitting a 27B Model Beat Two Replicas by 2 to 4x

We had a rule that a model which fits on one GPU should run as one replica per GPU. On a two-RTX-4090 machine with a 27B model, that rule was wrong by a factor of two to four, and the reason was not the parallelism at all. Here are the measurements, the rule that replaced it, and how the router now sends a 40k-token prompt to the worker that can hold it.

Sep 8, 20266 min read

Pay for the Tokens You Use. Nothing to Cancel.

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.

Sep 7, 202610 min read

Every Inference Request Pays the GPU That Ran It

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.

Sep 6, 202610 min read

One Config Is Always the Weakest Card's Config

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.

Sep 2, 20267 min read

Two Ways Your GPU Earns Here

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.

Sep 1, 20266 min read

Where Does Your Inference Actually Run?

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.

Sep 1, 20265 min read

A URL That Lives as Long as the Rental

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.

Aug 31, 20268 min read

The Faster Card Was Half the Speed

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.

Aug 24, 20269 min read

We Measured It All Along and Never Used It

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.

Aug 23, 202610 min read

How We Prove a GPU Is Real

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.

Aug 22, 202615 min read

Inside the Paralon Engine: One Agent, One Scheduler, Any AI Workload

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.

Aug 22, 20268 min read

Image Generation API: Z-Image-Turbo, OpenAI-Compatible, ~5 Seconds per Image

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 to try, $0.003 per image after. Try it from curl, Python, or straight from our Discord.

Aug 20, 20264 min read

Qwen 3.8 27B Is Live: a Free, OpenAI-Compatible Vision LLM API

Our inference API can now see. Qwen 3.8 27B — natively multimodal, 32k context, tool calling — is live on the ParalonCloud API, with a free trial key. Send screenshots and images through the same OpenAI-compatible endpoint you already use.

Jul 14, 20263 min read

Rent GPUs From Inside Claude — the ParalonCloud MCP Server

You can now rent a GPU without leaving your editor. The ParalonCloud 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.

Jul 13, 20264 min read

Rent a GPU With One API Call — Introducing the ParalonCloud Rental API

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.

Jul 12, 20267 min read

Paralon vs Vast.ai for GPU Hosts: Payouts, Fees & Setup Compared

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.

Jun 16, 20264 min read

Render Blender in the Cloud on a Decentralized GPU Render Farm

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.

Jun 12, 20266 min read

Run OpenClaw on a Free LLM API (Full Qwen 3.6 Setup)

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.

Jun 11, 20264 min read

Run pi, the Open-Source Coding Agent, on Your Own LLM API

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.

Jun 10, 20264 min read

Set Your Own GPU Rental Price — Your Hardware, Your Rules

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.

Jun 8, 20263 min read

The ParalonCloud Incentivized Testnet: Earn Points in Season 0

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.

Jun 1, 20268 min read

Monetize Your NVIDIA GPU on Paralon

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.

May 28, 20266 min read

How to Mine Pearl (PRL) by Renting GPUs on Paralon

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.

May 19, 20265 min read

A Field Guide to Updating a Network You Don't Own

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.

May 19, 20266 min read

One VRAM Number Can't Schedule LLMs Across Mixed Consumer GPUs

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.

Apr 30, 20265 min read

Run AI on Your Own Infrastructure: Why Self-Hosted Inference Is the Enterprise Default

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.

Apr 22, 20263 min read

Paralon Cloud Partners with Romanian-American University to Power AI Research and Education

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.

Mar 1, 20263 min read

Introducing Paralon Cloud: Decentralized GPU Infrastructure for Everyone

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.