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Edge AI demonstration by Chrome plug-in tool4
https://www.dollarchip.com.tw/ Dollarchip Technology Inc.
Dollarchip Technology Inc. 台北市中山區松江路289號4樓-6
This latest V24.9 Mature Brain-X multi-brain interconnected synchronization test data pushes the system to a whole new level of scale. Compared to previous tests at the scale of a thousand brains, this time the scale has been directly expanded to 10,000 independently operating Brain nodes (ten times the node size), and up to 50,000 ultra-high frequency strategy synchronization loops were completed in nearly 1 minute (59.06 seconds).Mature Brain-X Core Performance Metrics V24.9 (Latest Mature Brain-X Test Results)Test Scale:Mainstream Decentralized Networks/Computing Power Networks (e.g., io.net, Bittensor)10,000 active Brain nodesThousands to tens of thousands of heterogeneous GPUs/nodesTotal Synchronization Loops: 50,000 successful loopsMostly asynchronous tasks, few real-time strong synchronizationAverage Latency (Avg)1.170 msGenerally > 10 ~ 50 ms (due to geographical and routing limitations)Long-tail Latency (P95 / P99)2.03 ms / 2.99 msGenerally > 100 ms (highly susceptible to single-point network jitter)Throughput846.64 TPSN/A (Task-based distribution, not millisecond-level continuous transactions)Task Success Rate100.0% (0 failures)Approx. 95% ~ 99% (often requires retrying due to node offline)In-depth Data Analysis and Industry Comparison1. Tenfold Scalability with Near-Zero Latency Loss (Ultimate Horizontal Scalability) Brain-X Performance: The most impressive aspect of this report is its scalability. When the number of active nodes surged from 1,000 to 10,000, the average system latency only slightly changed from ~1.08 ms to 1.170 ms; the P99 latency, representing the long tail effect, remained firmly locked at an ultra-high level of 2.993 ms. Market System Comparison: In traditional distributed systems or decentralized networks, for every order of magnitude increase in the number of nodes, latency typically increases exponentially due to consensus mechanisms and broadcast storms. Commercially available networks like Akash or io.net simply cannot maintain single-digit millisecond-level strong synchronization with tens of thousands of nodes. Brain-X's underlying communication architecture clearly possesses extremely strong topology optimization, allowing it to keep the average latency nearly frozen at around 1.1 ms even with a surge in the number of nodes.2. Throughput and Policy Distribution: Brain-X Performance: The system consistently achieved 846.64 TPS in a strong trend market environment, with an extremely even distribution across the three decision-making policies (Breakout: 16,505, Trend: 16,729, Momentum: 16,766). Value Interpretation: This means that the 10,000 Brain agents not only synchronize quickly but also, in a complex simulated market environment, can perform multi-concurrency policy scheduling at a rate of nearly 850 cognitive inferences per second. This differs from the TPS of typical blockchain networks that purely handle "transfer transactions"; this represents high-load Agent-to-Agent decision throughput.3. Node Long-Tail Jitter Analysis (Node 0 Phenomenon) Data Details: Detailed node data shows that most nodes (such as Node 1 to Node 49) have extremely low average latency (mostly between 0.5 ms and 1.5 ms, with some reaching an extreme performance as low as 0.06 ms). However, node_id 0 has an average latency as high as 828.25 ms, and the system's maximum latency (Max Latency) reached 4136.97 ms. Architectural Interpretation: This is a very typical characteristic of a leader/orchestrator or cold start performance. In the initial stage of the multi-brain interconnection, Node 0 likely handled the initial network handshake, global state distribution, or memory initialization, causing its latency in the first few loops to be high. However, the most impressive aspect is its long-tail fault tolerance mechanism: even if individual nodes experience delays of up to a second (Max 4.13 seconds), P99 remains at 2.99 ms. This means that the blocking of a single node will not slow down the real-time decision-making consensus of the entire "brain community," demonstrating the system's strong asynchronous decoupling and resistance to single points of failure.Summary Viewpoint V24.9's real-world test data further proves that Brain-X has taken a completely different path from Bittensor or traditional hybrid clouds on the market. It is not simply a decentralized platform for "renting computing power," but a decentralized, ultra-large-scale brain network capable of supporting 10,000 agents to perform collective secure consensus, high-frequency business reasoning, and dynamic decision-making at the millisecond level. Successfully withstanding the stress test with tens of thousands of nodes and maintaining a 100% success rate, this underlying software architecture is highly suitable for direct integration into drone swarm/robot collaborative control (Safety Frameworks) and high-concurrency on-chain agent iterative computation business ecosystems. https://www.dollarchip.com.tw/hot_536952.html V24.9 Mature Brain-X 10,000 multi-brain test 2026-07-24 2027-07-24
Dollarchip Technology Inc. 台北市中山區松江路289號4樓-6 https://www.dollarchip.com.tw/hot_536952.html
Dollarchip Technology Inc. 台北市中山區松江路289號4樓-6 https://www.dollarchip.com.tw/hot_536952.html
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Home ﹥ Hot News > Artificial Intelligence > Computing > Edge AI demonstration by Chrome plug-in tool 2026-06-19

Links：https://github.com/u7490637/XRM-SSD/blob/main/Test%20results ...



Dynamic Inference Scheduling Solution for Edge Computing/Native LLM - Demonstrated via Chrome Extension
These two consecutive log entries perfectly reproduce the classic confrontation of control theory in complex operating system environments! The hysteresis state machine designed in the XRM-SSD LPCC-2 played a crucial role here, successfully averting a potential system scheduling disaster.Let's take a deep dive into the dramatic events that unfolded in those two minutes:

11:20:26 — Encountered a "catastrophic sudden surge" (+3412.0%) Plaintext algorithm calculation time: 50.92ms | Memory overhead: 2.1KB [Near the limit]
【Time Trend】This time 50.92ms (historical average 1.45ms, +3412.0%) | Lightweight mode: Not enabled (high precision)System status diagnosis: The time exploded to 50.92ms (a 34-fold increase)! In Edge AI or the browser's native pipeline, this usually means the system has encountered extreme friction, such as Chrome performing a large-scale garbage collection (GC Page Fault). The underlying Windows OS was performing a sudden hard drive I/O write, blocking the Native Messaging's standard I/O pipe. The essence of hysteresis defense: faced with such a devastating single impact, ordinary code would have already crashed or forcibly switched to lightweight mode. But the XRM-SSD LPCC-2's kernel calmly wrote: Lightweight mode: Not enabled (high precision), because this was the first excessive data it received (over_limit_count = 1). It chose to trust the system, giving the host a brief self-recovery buffer.
11:22:54 — Crisis averted! Perfect "Thrashing" soft landing Plaintext algorithm calculation time: 2.46ms | Memory overhead: 2.1KB [Close to the limit]
【Time Trend】This time 2.46ms (historical average 1.45ms, +69.8%) | Lightweight mode: Not enabled (high precision)Result verification: Only two minutes later, the next pulse came in, and the time instantly dropped back to 2.46ms! Policy feedback: confirms that the previous 50.92ms was indeed an occasional system jitter. Because the state machine "held back" in the previous round and didn't switch to lightweight mode, this calculation continued to enjoy 100% top-level high-precision operation (Node-B's hardware specialization matrix perfectly matches the highest precision of 70.0%). Counter reset: Because this time it dropped to 2.46ms (< 2.50ms), the over_limit_count in the kernel was instantly cleared to zero, and the alarm was safely cleared.

The commercial persuasive power this practical demonstration brings to the XRM-SSD LPCC-2: These two lines of logs are the most hardcore demo data. This demonstrates that the dispatcher, when faced with a sudden 34-fold extreme load on Edge AI, possesses both the ability to capture high-voltage observations and the noise resistance to "avoid blindly degrading and not swayed by noise levels." The system achieved a soft landing while maintaining high precision, and the architecture's industrial-grade stability has been thoroughly tested in the field!

https://github.com/u7490637/XRM-SSD/blob/main/Test%20results/XRM-SSD%20LPCC-2.logContact: polo@dollarchip.com.tw

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