According to @deanmlittle, a new low-level tweak in the doppler-asm repository cuts execution by 1 compute unit (CU) and trims 16 bytes from the binary, indicating micro-optimizations at the assembly layer that can reduce runtime and code size; source: X post by @deanmlittle (twitter.com/deanmlittle/status/1966766713233395795), source: GitHub blueshift-gg/doppler-asm (github.com/blueshift-gg/doppler-asm). On Solana, prioritization fees are calculated as microlamports-per-CU multiplied by total CUs, so shaving CUs directly lowers per-transaction priority fees for the same priority level, which is trading-relevant for cost-sensitive DeFi execution; source: Solana Docs on prioritization fees (docs.solana.com/transaction_fees#prioritization-fees), source: Solana Compute Budget Program (docs.solana.com/developing/runtime-facilities/programs#compute-budget-program). Reducing binary size can also lower program deploy or upgrade costs on Solana because program accounts must hold a rent-exempt balance proportional to data size, meaning smaller binaries require less SOL locked as rent-exempt collateral; source: Solana Accounts and Rent Exemption (docs.solana.com/developing/programming-model/accounts#rent-exemption), source: Solana Program Deployment overview (docs.solana.com/deploying/programs). Net takeaway for traders: even modest CU and byte-size savings can translate into lower fees and leaner capital requirements when operating on Solana, improving cost efficiency for on-chain strategies during network congestion; source: Solana Docs on prioritization fees and compute budget (docs.solana.com/transaction_fees#prioritization-fees, docs.solana.com/developing/runtime-facilities/programs#compute-budget-program).

According to @deanmlittle, a recent post indicates @switchboardxyz oracle updates were around ~120 CUs and, after collaboration with @DoctorBlocks, a permissionless path has reached 27 CUs (source: @deanmlittle on X). Based on the figures shared, that is a reduction from ~120 to 27 CUs per update, implying roughly a 77.5% drop and about a 4.4x efficiency gain per update (source: @deanmlittle on X). The thread also notes a 21 CU permissioned prototype but emphasizes the 27 CU implementation remains permissionless (source: @deanmlittle on X). Traders tracking oracle-integrated protocols may monitor for any official confirmation and rollout details, as the post highlights developers “pushing the limits of program efficiency” (source: @deanmlittle on X).

According to @deanmlittle, after reverse engineering a newly released dark AMM, he concluded the implementation is not his code and that it wastes 6 CUs, indicating non-optimal compute efficiency per interaction (source: @deanmlittle on X, Aug 29, 2025). For traders and routing algorithms, the reported 6 CU waste indicates added on-chain overhead that affects execution efficiency and cost-sensitive order flow when interacting with this AMM (source: @deanmlittle on X, Aug 29, 2025).

According to @deanmlittle, the question is why tiered storage is a real problem on Solana given consumer NVMe claims of over 2 million 4KB random read IOPS and whether the protocol should care if slow validators are already punished. Solana’s AccountsDB tiered storage offloads cold state to disk, but the design explicitly warns that higher disk read latency can slow account loads during banking, especially under mixed read/write and low-queue-depth workloads that matter to leaders, which makes advertised peak IOPS a poor proxy for effective throughput in production (source: Solana Labs RFC on AccountsDB tiered storage, GitHub). Solana leaders have roughly 400 ms per slot to fetch accounts, execute transactions, and propagate blocks, so p99 latency spikes on disk-backed state can push leaders over deadline even when average SSD IOPS look high (source: Solana whitepaper on Proof of History and slots by Solana Labs). Compute Units are bounded per transaction and per block via the Compute Budget Program, so state-read latency becomes a bottleneck irrespective of higher CU ceilings because execution stalls on account reads and locks when storage is too slow (source: Solana Docs, Compute Budget Program; Solana Runtime docs on accounts and locks). While slow voting and missed block production reduce a validator’s vote credits and rewards, a slow leader still occupies scheduled slots and can elevate fork rate and confirmation times before penalties are realized, so the protocol sets expectations to maintain baseline liveness and throughput for all participants (source: Solana Docs, Staking and Rewards; Solana Docs, Leader schedule and consensus overview). Solana Foundation hardware guidance emphasizes high-performance NVMe and large RAM footprints to keep hot state in memory and minimize tail latency, underscoring that storage tiering must be engineered around leader-time constraints rather than headline SSD IOPS (source: Solana Foundation/Docs, Validator hardware recommendations). For traders, congestion from slow state reads drives priority fees and execution uncertainty on Solana; fees rise with contention under local fee markets, making performance-sensitive storage decisions directly relevant to SOL’s on-chain cost and throughput profile (source: Solana Docs, Transaction fees and priority fees; Solana Docs, Local fee markets and congestion behavior).

According to @bolsaverse, AO's decoupled execution architecture is divided into specialized units that enhance trading efficiency. Compute Units (CUs) execute tasks on demand, which can optimize processing speeds for trading algorithms. Messenger Units (MUs) handle inter-process messaging, crucial for maintaining real-time communication between trading components. Scheduler Units (SUs) manage sequencing and Arweave storage, providing robust data management and storage solutions critical for reliable trading operations.