Client: MediaTek

Industry: Semiconductor / Mobile Telecommunications

Solutions Deployed: Custom On-Device Performance Diagnostic Software (Dimensity Profiler)

Executive Summary

MediaTek partnered with WhaleFlux to engineer the Dimensity Profiler, a proprietary, on-device chip-level monitoring system. Designed specifically for MediaTek’s architecture on Android smartphones, the software empowers engineers and game developers to optimize CPU/GPU performance, automate debugging, and precisely locate hardware-software bottlenecks in heavy-duty mobile games. This deep-tier visibility ultimately drove a 45% reduction in debugging timeand unlocked a 20% uplift in gaming performance.

The Challenge

As mobile games push the limits of smartphone hardware, ensuring perfect synergy between game rendering and chip processing is critical. MediaTek needed a solution to address several core engineering hurdles:

Lack of Edge-Side Visibility

Standard monitoring tools could not capture the granular, chip-level telemetry required directly on the end-user’s device.

Manual Debugging Inefficiencies

Identifying exactly why a specific game caused frame drops or thermal throttling was a time-consuming, manual process for both MediaTek and allied game studios.

Hidden Performance Bottlenecks

Without deep CPU/GPU interaction data, it was difficult to locate the exact rendering bottlenecks preventing games from running at peak optimization on MediaTek silicon.

The Solution: How We Did It

WhaleFlux developed a highly specialized, lightweight on-device monitoring system tailored entirely to MediaTek’s chipsets, focusing on automated diagnostics and deep hardware integration.

On-Device Chip-Level Telemetry

We engineered an edge-side application that runs natively on Android, capturing real-time, ultra-precise data on CPU/GPU utilization, clock speeds, and thermal dynamics without interfering with gameplay.

Automated Debugging Protocol

The system continuously monitors for game-to-chip compatibility issues. When an anomaly or inefficient hardware usage is detected, it automatically triggers a data collection protocol, compiling comprehensive diagnostic logs instantly.

CPU/GPU Bottleneck Localization

By mapping game performance against chip-level data, the software accurately pinpoints exactly where rendering pipelines stall, allowing developers to see precisely how the CPU and GPU are handling specific gaming workloads.

The Results & Impact

The custom WhaleFlux monitoring system provided MediaTek and its developer ecosystem with the definitive tool for mobile game optimization.

45% Faster Debugging

Automated issue detection and the immediate generation of granular diagnostic logs eliminated the guesswork, cutting the time required to identify and resolve compatibility issues almost in half.

20% Performance Uplift

By successfully locating and addressing deep-seated CPU/GPU bottlenecks, MediaTek enabled game developers to optimize their code, resulting in up to a 20% increase in overall game performance and frame rate stability.

Proactive Ecosystem Support

MediaTek can now proactively monitor new game releases, ensuring their chipsets consistently deliver a top-tier, fully optimized experience for end-users worldwide.

Conclusion

Through its collaboration with WhaleFlux, MediaTek transformed the way mobile game performance is analyzed and optimized on-device. By enabling deep chip-level visibility and automated diagnostics directly on Android smartphones, the Dimensity Profiler empowers engineers and game developers to rapidly identify bottlenecks and fine-tune performance with unprecedented precision.

This partnership not only accelerated MediaTek’s internal debugging workflows but also strengthened its broader developer ecosystem, ensuring that mobile games can fully leverage the capabilities of MediaTek chipsets. As mobile gaming continues to demand higher performance and efficiency, the WhaleFlux-powered monitoring system provides MediaTek with a scalable foundation for ongoing optimization, helping deliver smoother, more immersive gaming experiences to users worldwide.

Client: Exabits

Industry: Decentralized AI Computing Infrastructure (DePIN)

Solutions Deployed: WhaleFlux Compute Power Management Platform

Executive Summary

Exabits, a leading decentralized AI compute network, partnered with WhaleFlux to streamline the management of its highly complex, large-scale GPU infrastructure. By deploying WhaleFlux’s Compute Power Management Platform, Exabits successfully orchestrated over 1,000+ full-series NVIDIA GPUs across multi-cloud environments. This collaboration transformed heterogeneous resource management, ensuring that high-performance computing (HPC) tasks are executed with maximum efficiency, enterprise-grade reliability, and optimized inference speeds.

The Challenge

Managing a massive, decentralized network of compute resources presents unique operational hurdles. To maintain its competitive edge and serve demanding AI developers, Exabits needed to overcome several critical bottlenecks:

Heterogeneous Resource Fragmentation

Integrating, managing, and scheduling a diverse array of multi-cloud, full-series NVIDIA GPUs without causing system latency or fragmentation.

System Stability for Heavy Workloads

AI training and inference require continuous, unbroken uptime. Exabits needed a way to monitor and maintain stability across decentralized nodes to prevent task failures.

Suboptimal Resource Utilization

Without an intelligent scheduling system, high-performance computing (HPC) environments are prone to GPU idling, bottlenecking, and inefficient power distribution.

The Solution: How We Did It

Leveraging our deep expertise in software development and advanced algorithms, WhaleFlux delivered a comprehensive, end-to-end compute management solution designed to handle Exabits’ massive scale.

Unified GPU Orchestration (1000+ Scale)

We deployed a centralized platform capable of supporting and intelligently scheduling over 1,000 full-series NVIDIA GPUs. This unified system seamlessly integrated multi-cloud and heterogeneous compute resources into a single, manageable pool.

Algorithmic Scheduling & Inference Acceleration

Utilizing WhaleFlux’s proprietary algorithms, the platform dynamically matches the specific requirements of each AI task to the most appropriate hardware. This intelligent routing accelerates inference times and dramatically optimizes overall compute efficiency.

Full-Stack Stability Monitoring

We implemented robust, full-stack monitoring and maintenance protocols. The platform provides real-time visibility into node health, automated error detection, and proactive maintenance alerts to ensure the reliable execution of long-running tasks.

The Results & Impact

The implementation of the WhaleFlux platform provided Exabits with the robust infrastructure management required to scale their operations confidently.

Maximized Compute Efficiency

Intelligent, algorithm-driven scheduling significantly reduced GPU idle time and delivered measurable acceleration for AI inference workloads.

Enterprise-Grade Reliability

The full-stack monitoring system established a highly resilient operational environment, minimizing downtime and ensuring consistent performance for Exabits’ end-users.

Scalable HPC Execution

WhaleFlux’s software architecture established a future-proof foundation, enabling Exabits to effortlessly manage increasingly complex high-performance computing tasks as their network continues to grow.

Conclusion

Through its partnership with WhaleFlux, Exabits successfully transformed the management of its decentralized AI compute network into a unified, intelligent, and highly scalable system. By combining advanced scheduling algorithms, full-stack observability, and centralized GPU orchestration, WhaleFlux enabled Exabits to unlock the full potential of its distributed infrastructure while maintaining the stability and performance required for demanding AI workloads.

As Exabits continues to expand its decentralized compute ecosystem, the WhaleFlux Compute Power Management Platform provides a resilient and future-ready foundation—empowering the network to support larger AI workloads, onboard more global compute contributors, and deliver reliable high-performance infrastructure to developers building the next generation of AI applications.