Q vs ETH vs SOL

Quilibrium is a novel decentralized system that differs from traditional blockchains in both architecture and performance. While not technically a blockchain, it can be analyzed in parallel with Ethereum 2.0 and Solana to provide context on its capabilities. Below is a breakdown of their respective specifications, offering insight into how Quilibrium fits within the broader landscape of decentralized computing.

Quilibrium: Raw Specifications

• Clock Speed (per core): 54M OTs/s (~54MHz)

• CPU: SMP Multicore (~10^98 cores maximum), Garbled Circuits

• RAM: ~19kB Global, 1GB per core shard

• HDD: RAID6-like, max capacity of 1.8765 * 10^107B

Quilibrium’s architecture relies on sharded data storage and computation, designed to ensure privacy and security while maintaining high performance. It utilizes garbled circuits, a cryptographic technique that allows secure multi-party computation (MPC), which enhances security without exposing sensitive data. The extensive potential core count suggests a system built for parallelized, distributed workloads.

Ethereum 2.0: Raw Specifications

• Clock Speed: 30Mgas/4/12s (~650kHz)

• CPU: Single instruction, stack-based, EVM bytecode, Turing Complete

• RAM: Requires full transaction history (~1TB)

• HDD: 6 * 4096 * 32B sectors, 18-day in-network retention (~100GB)

Ethereum 2.0 aims to improve upon Ethereum’s previous inefficiencies by implementing proof-of-stake and sharding. However, it still relies on the Ethereum Virtual Machine (EVM), which is stack-based and processes instructions sequentially. This structure, while flexible and widely adopted, can be less optimized for high-speed execution compared to parallel architectures.

Solana: Raw Specifications

• Clock Speed: 48M CU/450ms (~106MHz)

• CPU: Single instruction, event-based, eBPF, Turing Complete

• RAM: Requires pruned history (~100GB)

Solana’s primary advantage is its high throughput, achieved via its Proof-of-History (PoH) mechanism. It leverages an event-driven model with eBPF (extended Berkeley Packet Filter), which allows for more efficient execution of smart contracts compared to traditional stack-based virtual machines. Solana’s clock speed is significantly higher than Ethereum’s, optimizing it for fast and frequent transaction processing.

Key Observations and Comparisons

Clock Speed & Computational Power:

• Quilibrium operates at ~54MHz per core, while Solana’s equivalent computation unit runs at ~106MHz. However, Quilibrium’s SMP Multicore design (scaling up to ~10^98 cores) suggests a fundamentally different approach, where parallelism is a core strength rather than raw per-core speed.

• Ethereum 2.0, at ~650kHz, runs significantly slower than both Quilibrium and Solana, primarily due to its reliance on the EVM and the need for full transaction history validation.

CPU & Execution Model:

• Ethereum 2.0 uses a stack-based execution model, which limits efficiency in complex computations.

• Solana’s event-driven approach via eBPF improves upon this, allowing for optimized execution.

• Quilibrium’s use of garbled circuits and SMP Multicore suggests a focus on secure, high-performance computing that scales across an extensive number of cores.

Storage & Data Management:

• Ethereum 2.0 requires full history storage (~1TB), leading to long-term scaling concerns.

• Solana prunes history aggressively (~100GB), improving efficiency but potentially limiting on-chain data availability.

• Quilibrium’s RAID6-like storage mechanism with an astronomical theoretical capacity (1.8765 * 10^107B) indicates a system optimized for distributed data redundancy and high availability.

Conclusion

While Ethereum 2.0 and Solana are both established blockchain systems, Quilibrium presents a fundamentally different model centered around parallel computation, privacy-preserving cryptographic techniques, and highly scalable architecture.

Rather than focusing on linear improvements in execution speed or transaction throughput, it introduces a design paradigm that prioritizes distributed, secure, and efficient multi-party computation. These distinctions make it an interesting alternative for applications requiring both privacy and high computational efficiency.