# Moving Beyond TPS

Performance is the assumed reason of existence of GSVM. But how do we measure it?

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.

**Real-life Peak Usage**

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.

To model such scenarios, we evaluate performance through the following lenses:

* **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, we evaluate execution under complex, interdependent workloads, the kind found in AMMs, AI agents, or large multiplayer games.


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