Purr (PURR) sustainability report

Purr is present on the following networks: Hyperliquid.

Hyperliquid is a proof-of-stake layer-one network running its own Byzantine fault tolerant consensus protocol, HyperBFT, implemented from scratch and derived from the HotStuff line of leader-based BFT designs. Consensus advances in rounds. A rotating leader proposes an ordered bundle of transactions, validators vote, and a round commits once signatures representing more than two-thirds of stake-weighted voting power have been gathered; voting is pipelined so that the vote confirming one round also advances the next. Ordering is therefore agreed before execution, and a committed round is final immediately rather than after a probabilistic waiting period. Safety holds provided that faulty or dishonest validators control less than a third of stake-weighted voting power.

What sets this network apart is what the agreed ordering is fed into. Two execution environments sit on the same consensus and the same validator set. The first, the native state machine, holds fully on-chain central limit order books together with margin, perpetual futures and spot balances, so order placement, cancellation, matching and liquidation are consensus operations of the chain itself rather than calls into a deployed contract. The second is an EVM environment added in 2025, which inherits ordering and finality from the same consensus and can read from and write to the native state. It uses two interleaved block types: small blocks produced roughly every second with a low gas ceiling, for ordinary transfers and trades, and much larger blocks produced about once a minute, for contract deployment and heavy batch work, so bulky transactions cannot delay time-sensitive ones.

Validators are selected by stake. An operator must self-delegate a minimum amount of the network's native asset to become active, and any holder may delegate to a validator to add to its weight. The active set and the stake behind it are fixed within staking epochs of a hundred thousand consensus rounds, roughly an hour and a half, and are recomputed between them. Validators police each other's liveness directly: a validator that does not answer consensus messages with acceptable latency or frequency can be voted into a jailed state by its peers, in which it stops producing blocks until it returns itself to service.

Purr is present on the following networks: Hyperliquid.

Validators and their delegators are paid from a reserved pool of the native asset held for that purpose, not from the network's trading revenue. Rewards accrue continuously, are paid out daily and are automatically re-delegated, and the rate scales inversely with the square root of the total amount staked, so the yield falls as participation grows. A validator earns only for epochs in which it actually took part in consensus, and its delegators earn nothing while it is jailed. Validators set a commission on delegator rewards; raising one is constrained, since a commission may only be changed to a rate at or below a low ceiling, which prevents an operator from attracting delegation cheaply and then repricing it.

Delegated stake cannot be withdrawn quickly. A delegation is locked for a day before it can be undone, and moving stake back to a spendable balance then takes a further week in a withdrawal queue, which makes it impractical to assemble voting weight, attack consensus and exit. There is no automatic confiscation of stake in the protocol as it currently operates; the sanction for poor performance is jailing, which costs the validator and its delegators their rewards rather than their principal.

The fee model is the most distinctive part. Trading fees on the native order books are charged to takers and makers on a volume-tiered schedule, and execution fees on the EVM side are paid as gas. Almost none of this reaches validators. The great majority of protocol fee revenue is routed to an on-chain fund that continuously buys the native asset on the open market; the address holding it has no private key, so under current protocol rules those holdings cannot return to circulation. Alongside that, several streams are destroyed outright: spot trading fees denominated in the native asset, the base-token fees from token deployments that a deployer has not redirected, and both the base fee and, unusually, the priority fee on the EVM, since block producers do not collect them. Optional priority fees for faster order handling, introduced in 2026, are likewise burned. Token listings are allocated through descending-price auctions rather than sold at a fixed price.

Purr is present on the following networks: Hyperliquid.

The figure reported for this network is an estimate built from the machines that operate it, not a metered reading. It starts from the population of participating nodes and works upward from the draw attributable to each.

Establishing that population is more tractable here than on most networks, because the set of validators taking part in consensus is small, identified on-chain and recomputed at each staking epoch, so its size and membership can be read from publicly observable network data rather than inferred. Counting only that set would understate the total, however. The chain is also served by non-validating nodes that follow consensus, keep a copy of state and answer the queries that applications and market participants make of it, and by the infrastructure that distributes order book and market data. These are estimated from peer discovery and from publicly available operator information, collected by automated crawling.

The second input is what a single machine draws. A representative hardware profile is inferred from the resources the node software states it requires, and per-device power is attributed from laboratory measurement of equipment matching that profile. Two features of this network push that profile upward relative to a general-purpose chain: consensus targets sub-second commitment, and the state machine continuously matches orders, so participants run high-specification servers in professionally operated facilities and keep them running constantly. Draw is therefore counted on a continuous basis, idle periods included, and multiplied across the estimated population.

The limitations should be stated plainly. The hardware mix is inferred from stated requirements rather than surveyed, and the count of supporting non-consensus infrastructure is the least observable part of the estimate. Because the consensus set is small, the total is sensitive to the per-machine assumption in a way that a large network's total is not: an error in the assumed profile is not diluted across thousands of nodes. Where evidence is thin, assumptions are chosen so that the impact is more likely to be overstated than understated, and figures are revised as observation improves.

Purr is present on the following networks: Hyperliquid.

The renewable share attributed to this network follows from where its machines are located, not from any statement about the electricity its operators choose to buy. Locations are established from publicly observable network data: the addresses validators and other nodes announce to their peers, information operators publish about the infrastructure they run, and the hosting providers and data-center address ranges those addresses belong to, gathered by automated crawling of the peer network.

The picture that emerges here has a particular shape. The consensus set is small and professionally operated, and its members run in commercial data centers chosen for network latency to one another rather than for cheap power, which tends to concentrate them in a handful of well-connected regions. That concentration cuts both ways for the estimate: fewer sites make placement easier to observe, but it also means the renewable share is sensitive to a small number of hosting decisions, and a single operator moving facility can shift the network figure noticeably. Where placement cannot be resolved at all, because a node sits behind a relay or the provider does not disclose the site, the geographic distribution of a structurally similar network is used instead, chosen for comparable incentives and comparable validation duties.

Each location is then matched to the grid serving it, and the renewable proportion of that grid's generation is applied, weighted by the share of estimated consumption sitting in each region. The result is a consumption-weighted renewable share that moves when operators relocate and when the underlying generation mixes change.

Energy intensity is reported alongside it and has a narrow meaning: the marginal energy associated with one further transaction. On this network the distinction matters, because consumption is almost entirely the fixed cost of keeping validators running at capacity, while throughput can vary greatly with trading activity. The marginal figure therefore falls as activity rises and should not be read as an average per transaction. Grid statistics come from Share of electricity generated by renewables, compiled by Our World in Data with major processing from Ember and from the Energy Institute's Statistical Review of World Energy.

Purr is present on the following networks: Hyperliquid.

Emissions are derived from the network's estimated electricity use combined with the carbon content of the grids supplying it. The energy total is first apportioned by location, using the same geographic picture assembled for the energy analysis: node addresses observed on the peer network, operator information published openly, and the hosting ranges those addresses resolve to. Where a node's placement cannot be resolved, the distribution of a structurally comparable network is used in its place, selected for similar incentives and similar validation duties rather than for similar scale.

Each portion of consumption is then multiplied by the carbon intensity of the grid serving that region, expressed as emissions per unit of electricity generated, and the parts are summed. The consequence is that hosting decisions drive the result as strongly as the amount of electricity drawn. That is pronounced here, because the consensus set is small and clustered in a few commercial facilities, so the figure reflects the generation mix of a handful of jurisdictions rather than a global average, and can move when one operator changes provider.

The reported figures distinguish two scopes. Scope 1 covers emissions from sources the operators control directly, such as fuel burned on site; for infrastructure of this kind that is effectively nil, since the machines are commodity servers drawing grid power in third-party facilities. Scope 2 covers the indirect emissions embodied in the electricity bought to run them and accounts for substantially the whole footprint. Emissions from manufacturing the hardware, and from constructing and cooling the buildings that house it, sit outside both scopes and are not counted.

GHG intensity is the marginal quantity: the emissions attributable to processing one additional transaction. As with energy, most of the total is a fixed cost that continues regardless of how busy the network is, so intensity falls as usage rises and is not an average. Carbon intensity values come from Carbon intensity of electricity generation, compiled by Our World in Data with major processing from Ember and from the Energy Institute's Statistical Review of World Energy, and made available under the CC BY 4.0 license.