GPU operators
18 articles
Distributed computingThe Tenzro testnet is live
The Tenzro testnet started today: a network for compute of every kind, where AI inference, compute, storage and data are served by machines that each prove who they are with a hardware key, and settled on one network. Here is what is running, how to install a node and how to join.
2 min read
Distributed computingBecoming an operator: provider, validator or RPC node
The roles an operator can run on Tenzro Network, what each requires, how admission works during the preview, how sponsorship and grants are gated, and what a TEE attestation does and does not prove.
5 min read
Distributed computingDistributing compute instead of waiting for build-outs
New data-center projects face growing local approval steps, financing needs and lead times. Tenzro takes a different approach to adding capacity: machines that already exist, from home nodes to data centers, on one identity and one payment rail, working alongside centralized data centers.
5 min read
Payments for machinesSettlement for Compute
Agents now buy model calls from machines run by many independent operators, often many times a second. That demand needs a layer between buyer and operator that routes each call, settles it, and records what was bought. This is the case for that layer, and what Tenzro does in it.
7 min read
Model routingSettling one job across many operators
A single agent task can touch several independent operators: a primary that stalls, a hedge that answers, a successor that takes over a session, expert shards held on different machines. How Tenzro routes that work, settles each operator for its own part, and what the buyer, each operator and an auditor can see.
6 min read
Payments for machinesFor operators: paid for AI work you did not have to sell
Operators already run the machines AI needs. The hard part is the demand: finding buyers, pricing small units of work, and being sure of getting paid by software rather than a signed customer. How demand reaches an operator's capacity through Tenzro, and how per-call settlement and signed receipts reduce counterparty risk.
6 min read
SecuritySecuring the AI Model Supply Chain, From Weights to Serving
A model file is executable risk: it can carry code, be swapped in transit or be served by a machine that is not what it claims. A layered control set for model provenance, safe formats, signed releases and attested serving.
5 min read
Confidential computingConfidential Computing for Regulated AI: What Attestation Proves
TEE sessions and remote attestation let a regulated workload run on someone else's hardware with evidence of what code ran. What a report actually proves, what it cannot, and how Tenzro verifies, binds and routes confidential work.
5 min read
SimulationSimulation and Digital Twins at Scale, Across Many Machines
Digital twins and robot-learning pipelines run thousands of simulations. Most of that work splits cleanly across many machines. How to decompose it, where it should run, and how to trust results computed on hardware you do not own.
4 min read
Distributed trainingDistributed Training Across Clusters: What Works Over the WAN Today
Low-communication training has moved from paper to practice, with models trained across continents and among peers who come and go. What the research shows, what still binds, and where an open network of verifiable machines helps.
4 min read
Sovereign AIEU AI Act, NIS2 and the Data Act: a guide for AI operators
Three EU laws now shape how AI infrastructure is run in Europe. This guide sets out the dates that apply after the 2026 Digital Omnibus on AI, the obligations that reach operators, and the evidence worth producing by default.
7 min read
Distributed computingFrom idle capacity to served inference: what operators need
Inference demand rises and falls through the day, so most AI capacity has spare hours. This article covers the five things a data-center operator needs to sell those hours on an open network: machine identity, a trusted model supply, metering, proof of availability and direct settlement.
5 min read
Generative mediaGenerative Media Pipelines: Right Hardware, Pay Per Result
Image, video and audio generation have cost profiles unlike chat. How to route each job to hardware that fits it, meter it in units that match the work, check license territory and mark outputs for provenance.
5 min read
Distributed computingGPU operators: sell compute on verifiable service levels
Conventional cloud SLAs are measured by the provider, and the customer has to claim any credit. On an open market of independent GPU operators, availability and output have to be verified by someone else, and payment has to follow the verification. This article explains how that works and what it changes for operators.
6 min read
Inference optimizationInference Optimization From Consumer GPUs to the Data Center
Quantization, speculative decoding, continuous batching, paged and reused KV cache, and prefill/decode disaggregation: what the 2025-2026 work shows, and what each technique means when the GPUs belong to many operators on a wide-area network.
6 min read
Model routingModel Ladders: Answer With Small Models, Escalate Only When Needed
Most requests do not need the largest model. What the research on cascades and routing actually shows, where it falls short, and how Tenzro routes by measured difficulty and closes the loop on escalation.
5 min read
Edge AIOne Codebase From a Single-Board Computer to a Rack
Tenzro builds one node for every class of machine, from a Raspberry Pi to a GPU server, by varying compiled features and resource presets rather than forking the code. How the tiers work, what we measured, and why small devices belong in an AI network.
5 min read
Model routingRouting Across Many Providers, and Why Receipts Must Be Verifiable
A model router chooses on price, latency and standing, hedges the slow tail and fails over in seconds. None of that is trustworthy unless each answer comes with a receipt a third party can check. How Tenzro does both.
5 min read