Eclipse Performance Thesis: GigaCompute in Action

Blockchains have historically underutilized modern compiute. While industries like AI have embraced GPUs, FPGAs, and SmartNICs to achieve orders of magnitude throughput gains, most blockchains continue to optimize for outdated hardware assumptions and rigid execution models.

Eclipse introduces GigaCompute: a new compute paradigm that rethinks blockchain performance from first principles, leveraging the freedom of being a modular Layer 2 to co-design software and hardware across the stack.

At the center of this thesis is GSVM, a high-performance, hardware-aware SVM client built to scale with modern infrastructure.

Moving Beyond TPS

Most blockchains benchmark performance with Transactions Per Second (TPS). While simple, TPS hides deeper limitations in execution design. A system optimized for basic token transfers may break down under real-world workloads.

Eclipse instead evaluates performance based on:

• Compute Units (CUs) Inspired by Solana’s model but extended to reflect concurrency, hardware acceleration, and execution efficiency. GSVM tracks work done across CPUs, GPUs, and offload engines like SmartNICs. Lock-heavy or non-deterministic workloads are penalized to discourage anti-patterns.

• End to End Latency Measured from submission to finality, not just at the execution layer. This matters for applications like games, perps, and real-time systems where jitter or delay breaks UX.

• Throughput Under Pressure Instead of counting empty transfers, Eclipse evaluates execution under complex, interdependent workloads, the kind found in AMMs, AI agents, or large multiplayer games.

Layer 1 Bottlenecks and the Opportunity for L2s

Most Layer 1s focus on optimizing consensus and rely on off the shelf hardware. This leads to architectural constraints:

• Serialized execution models that limit concurrency

• Inefficient memory access and cache patterns

• Resource contention between unrelated applications

As a Layer 2, Eclipse decouples performance from consensus. Settlement and finality are delegated to Ethereum, allowing GSVM to fully re-architect the execution stack.

GSVM: Engineering GigaCompute

GSVM is optimized for performance at every layer:

• Network Layer: SmartNIC based packet filtering, probabilistic pre confirmations, and application specific sequencing reduce latency at ingress.

• Runtime: A self improving runtime adapts to workload patterns via reinforcement learning. Hybrid concurrency models support resource isolation and reduce contention between apps.

• Storage: NVMe optimized databases, hardware aligned memory layouts, and sequencer driven caching minimize I/O bottlenecks and enable fast state access.

Together, these choices enable GSVM to deliver not just more throughput, but throughput that scales with application complexity.

Real World Motivation: The $TRUMP Memecoin Incident

In early 2025, the $TRUMP memecoin launch on Solana highlighted the risk of shared execution bottlenecks:

• AMM activity dominated shared resources

• Validators like Jito experienced degraded performance

• Priority fees surged ~50x

• Compute units per block dropped by 50%

Solana didn’t crash, but it buckled. Applications paid the price for contention in a shared execution environment.

Eclipse addresses this by introducing Hotspot Islands: isolated execution zones with dedicated compute threads and local fee markets. High volume apps are siloed from the rest of the network, allowing the system to remain stable even during traffic spikes.

Why GigaCompute Matters

GSVM’s architecture is tailored for workloads that demand concurrency, low latency, and scalable compute:

• AI: ML agents, inference, and coordination among on-chain models

• Gaming: Tick based updates, user input responsiveness, physics simulations

• DePIN: Sensor networks, data aggregation, and proof submission

All three domains require consistent performance under stress, the very scenario GSVM was built for.