The Ethereum Foundation has announced the activation of BPO-1. This “blob-parameter-only” upgrade lifts Ethereum’s per‑block blob capacity and gives rollups more room to post data without a full hard fork.
What BPO-1 Changes on Ethereum
BPO-1 is part of the Fusaka upgrade sequence and tweaks only the configuration parameters that govern blob data, the space rollups use to post compressed transaction batches to Ethereum. The fork raises the per‑block blob target to 10 and the maximum to 15, up from the baseline introduced with Proto‑Danksharding, expanding adequate capacity for layer‑2 ecosystems like Optimism, Arbitrum, and Base.
Because BPO-1 adjusts parameters rather than core consensus logic, it arrives as a lightweight activation that piggybacks on the already-live Fusaka changes and PeerDAS (peer data availability sampling). PeerDAS lets nodes validate blob availability without downloading every byte, so the network can safely tolerate more blob throughput without overwhelming bandwidth or storage.
Fees and Layer‑2 Throughput
With more blobs available per block, competition for blob space should ease during peak periods, pushing down the data component of rollup fees and smoothing gas spikes for users bridging and trading on L2s. Developers expect a noticeable reduction in average posting costs for high‑volume rollups, which translates into cheaper swaps, mints, and transfers on those networks during surges in traffic.
Researchers stress that BPO-1 forms just the first step in a staged ramp‑up: telemetry from validators and clients will track orphan rates, gossip performance, and PeerDAS behavior under the higher blob load. If metrics stay within targets, BPO-2 will follow in January, pushing blob targets to 14 and the maximum to 21 per block, further expanding adequate block capacity for rollups.
The BPO design reflects a “measure then dial up” philosophy for Ethereum scaling: use small, parameter‑only forks to expand capacity instead of large, infrequent jumps that risk destabilizing the network.
By isolating blob parameters, client teams can ship narrow changes, operators can prepare with minimal configuration churn, and the community can reverse or pause further increases if unexpected stress shows up in metrics.
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