How MegaETH targets 15K–35K TPS in 7-day mainnet stress test

ambcryptoPublished on 2026-01-21Last updated on 2026-01-21

Abstract

MegaETH, a real-time EVM-compatible blockchain, is launching its mainnet on January 22nd with a 7-day stress test aiming to process 11 billion transactions. The project targets a sustained throughput of 15,000–35,000 transactions per second (TPS), having achieved nearly 47,000 TPS in testing. It also boasts a 10-millisecond block time, significantly faster than other blockchains. While prioritizing speed and low latency, concerns about decentralization and potential censorship risks due to centralized sequencing have been noted. The stress test will involve user interaction with gaming applications and backend transactions through a decentralized exchange. Following the test, the public mainnet will launch alongside select DeFi and consumer applications.

MegaETH, the real-time EVM-compatible blockchain, announced that it will launch its mainnet on the 22nd of January. Dubbed the MegaETH stress test, it aims to process 11 billion transactions in 7 days.

They were ” opening mainnet to users for several latency-sensitive apps while the chain is under intense, sustained load.”

The project aims to achieve performance levels comparable to high-speed blockchains such as Solana [SOL] while also providing extremely low latency and high throughput.

It has achieved nearly 47k transactions per second (TPS), noted growthepie in a post on X. MegaETH was targeting a sustained, true TPS of 15k-35k across the 7 days of the stress test.

“In the end, MegaETH will have the largest tx count in history across all EVM chains while users frictionlessly play with the chain.”

Messari reported that the MegaETH testnet achieved a 10‐millisecond block time, far faster than any other blockchain.

While the design prioritizes speed, the report raised concerns about decentralization and potential censorship risks due to centralized sequencing.

MegaETH to push the boundaries of blockchain capabilities

“Stress tests only matter if they’re uncomfortable”, said the blockchain’s post on X. During the test, users can interact with gaming applications such as Stomp.gg, Smasher.fun, and Crossy Fluffle.

On the backend, the team will push a mix of ETH transfers and v3 automated market maker swaps through the decentralized exchange Kumbaya.xyz.

The public mainnet will launch after the global stress test. A selection of day-one DeFi and consumer applications powered by its native stablecoin, USDm, will also be launching.

Messari also documented that in October 2025, MegaETH raised $50 million during the MEGA token sale, which became oversubscribed within minutes. This figure was part of the nearly $75 million raised from various grassroots funding efforts.


Final Thoughts

  • MegaETH is an EVM-compatible blockchain aiming to deliver real-time crypto performance, with a 10 ms blocktime and nearly 47k TPS in testing.
  • The global stress test targets a total of 11 billion transactions in 7 days, starting on the 22nd of January.

Related Questions

QWhat is the main goal of MegaETH's 7-day mainnet stress test starting on January 22nd?

AThe main goal is to process 11 billion transactions in 7 days, targeting a sustained true TPS of 15,000-35,000.

QWhat key performance metrics has MegaETH achieved in testing according to the article?

AMegaETH has achieved nearly 47,000 TPS and a 10-millisecond block time in testing.

QWhat are some of the applications users can interact with during the MegaETH stress test?

AUsers can interact with gaming applications such as Stomp.gg, Smasher.fun, and Crossy Fluffle.

QWhat concerns did the Messari report raise about MegaETH's design?

AThe report raised concerns about decentralization and potential censorship risks due to centralized sequencing.

QHow much funding did MegaETH raise during its MEGA token sale in October 2025?

AMegaETH raised $50 million during the MEGA token sale, which was part of nearly $75 million raised from various grassroots funding efforts.

Related Reads

Has the 'Digital Gold' Narrative for BTC Failed?

**Title: Has the "Digital Gold" Narrative for Bitcoin Failed?** The article argues that Bitcoin's "digital gold" narrative remains valid despite a recent sharp price decline (from a peak near $126k in Oct 2025 to briefly under $61k in Feb 2026). It presents a long-term investment framework based on three core points: **1. Viewing Bitcoin as an Asset:** Bitcoin is presented as a superior potential store of value compared to gold. Key arguments are its absolute scarcity (21 million cap), superior portability, and transparent auditability via its public ledger. While acknowledging its current use in early, volatile stages (~3-4% global adoption), the author draws parallels to the early, disruptive phases of the internet and e-commerce. **2. Understanding the Recent Downturn:** The current ~50% correction is framed as a predictable, consensus-driven cycle following its post-halving peak (the 2024 halving preceded the Oct 2025 high). A crucial factor is a historic "changing of hands": the influx of new institutional buyers via ETFs allowed early, low-cost holders (miners, OG believers) to take profits. The author notes that while severe, Bitcoin's historical drawdowns (e.g., 93% in 2011, 77% in 2021-22) have been progressively smaller, suggesting maturing holder structure and decreasing volatility over time. **3. The Long-Term Perspective:** The long-term thesis hinges on Bitcoin capturing a portion of gold's market value. With Bitcoin's market cap at ~$1.4 trillion (at $70k) versus gold's ~$20 trillion, significant upside potential exists if the "digital gold" narrative is partially realized. However, the author strongly cautions that short-term risks remain, the bottom is unpredictable, and high volatility is inherent. The real risk is not Bitcoin failing but poor personal position management (over-leverage, wrong capital) and a lack of deep understanding, which can force investors out during severe downturns. The conclusion uses Amazon's 95% crash post-2000 dot-com bubble and subsequent 42x recovery as an analogy. The ultimate question is not if Bitcoin's price will rise, but if an investor's strategy and conviction can withstand the volatility to see the long-term play out. The recent divergence (gold up, Bitcoin down) is posed not as a narrative failure, but as potential evidence of this ongoing, painful transition from a speculative asset to a mainstream allocation.

marsbit31m ago

Has the 'Digital Gold' Narrative for BTC Failed?

marsbit31m ago

Has BTC's 'Digital Gold' Narrative Failed?

The article discusses Bitcoin's "digital gold" narrative, its recent price drop, and long-term outlook through the perspective of "Jason". It argues the narrative is not a failure but that Bitcoin represents a superior, new asset class due to its fixed supply (21 million), portability, and auditability. The piece compares its current ~3-4% global adoption rate to early internet/e-commerce, suggesting significant growth potential. Regarding the 2025-2026 price decline (from ~$126k to briefly under $61k), the author views it as a predictable, consensus-driven sell-off within Bitcoin's ~4-year cycle post-halving, exacerbated by a major "handover" from early, low-cost holders to new institutional buyers via ETFs. A key observation is that historical peak-to-trough drawdowns have lessened over time (e.g., 93% in 2011 to ~50% in 2026), indicating maturing volatility as holder structure changes. For the long term, the author uses a simple framework: Bitcoin's total market cap (~$1.4T at $70k) is only about 7% of gold's (~$20T). Even capturing 30-50% of gold's value would imply substantial upside. However, the article strongly cautions against viewing this as investment advice, emphasizing extreme volatility and the critical importance of risk management, position sizing, and deep fundamental understanding to survive severe drawdowns. It concludes by drawing a parallel to Amazon's 95% crash in 2000 and subsequent 42x recovery, stressing that the key is surviving market cycles to realize long-term potential.

链捕手41m ago

Has BTC's 'Digital Gold' Narrative Failed?

链捕手41m ago

From Code to Cognition: A Ten-Thousand-Word Guide to the Evolution of the Robot Brain

"From Code to Cognition: The Evolution of Robot Brains" The journey of robotic intelligence has shifted dramatically from manually coded systems to AI-driven brains. For decades, robots relied on layered software stacks—perception, state estimation, planning, control—each handcrafted. While predictable, they lacked adaptability. The 2010s saw deep learning revolutionize perception (e.g., object detection) and control (via reinforcement learning), but learned skills remained narrow. The arrival of Large Language Models (LLMs) marked a turning point. LLMs acted as high-level planners, interpreting natural language instructions and generating sequences of actions for traditional robotic systems to execute. However, true integration came with Visual-Language-Action (VLA) models, which fused vision, language, and motion prediction into a single network. Pioneered by models like RT-2 and open-source projects like OpenVLA, VLAs enable robots to reason and act directly from visual input and commands. The most advanced humanoid robots now employ a "dual-brain" architecture: a slow-thinking, large VLA (System 2) for reasoning and planning, and a fast-reacting, small network (System 1) for high-frequency motion control, sometimes with an even lower-level System 0 for balance. This split balances cognition with the physics of real-time movement. Computation is split between onboard hardware (e.g., NVIDIA Jetson) for safety-critical control loops and cloud/edge servers for non-critical tasks like learning and interfaces. A crucial driver is the open-source ecosystem—models like GR00T and OpenVLA allow startups to build upon pre-trained brains and fine-tune them with their own data, accelerating development. Despite progress, current systems struggle with recovery from errors, sample inefficiency, and long-horizon tasks. This has spurred the rise of **World Models**—neural networks that predict the consequences of actions. By simulating possible futures before acting (like NVIDIA Cosmos or Meta V-JEPA), robots can plan, recover, and generalize better. This represents the next frontier: shifting intelligence from learned reactions to an internal model of physics and cause-and-effect. The field is rapidly evolving. While not yet at its "ChatGPT moment," the convergence of cheaper hardware, scalable simulation, and world models points toward robots that are increasingly capable, adaptive, and useful. The question is shifting from "what can robots do?" to "what *should* they do?"

marsbit1h ago

From Code to Cognition: A Ten-Thousand-Word Guide to the Evolution of the Robot Brain

marsbit1h ago

AI Bubble Is Bursting

The AI Bubble is Bursting: A Necessary Purge on the Path to Ubiquitous Intelligence Market volatility has reignited debates about an AI bubble, with figures like Ray Dalio pointing to high valuations. However, this parallels the dot-com bubble, which, despite its crash, laid the physical infrastructure for today's internet era. The current AI investment frenzy, with tech giants planning trillions in infrastructure spending far outstripping current AI application revenues, appears similarly imbalanced. This 'bubble' is seen as an inevitable phase for a disruptive technology, paying the "innovation tax." Critically, AI inference costs have plummeted over 99.7% since 2023, making intelligence nearly free at the margin. This hasn't reduced spending but has instead unlocked massive new demand, as seen in enterprise AI cloud expenditure tripling. This follows the Jevons Paradox: efficiency gains lead to greater total consumption. The market is now entering a cleansing phase, weeding out speculative ventures lacking real moats. The deeper shift is a move from capital expenditure (CapEx) on hardware to value creation in operational expenditure (OpEx) through AI applications that solve real industry problems. While infrastructure valuations are high, rapid earnings growth from widespread AI adoption across sectors—from manufacturing and finance to law and healthcare—may digest these valuations over time. Ultimately, this creative destruction will leave behind robust infrastructure and optimized models, cheaply powering an AI-augmented future for all industries, much as the internet became indispensable after its own bubble burst. The core productive potential remains undiminished.

链捕手1h ago

AI Bubble Is Bursting

链捕手1h ago

Trading

Spot
Futures
活动图片