Fixstars AIBooster is a performance engineering platform designed to optimize AI workloads by enhancing GPU utilization, reducing bottlenecks, and cutting infrastructure costs.
Launched in early 2025, it offers two key modules: Performance Observability (PO) for real-time monitoring and visualization of system and AI job metrics across cloud and on-premises environments, and Performance Intelligence (PI) for automated and manual optimization, including GPU acceleration, hyperparameter tuning, and framework-specific enhancements.
Proven to deliver up to 2.5x speed improvements and 19% cost savings in real-world AI projects, AIBooster supports a wide range of industries—from autonomous driving to telecommunications—and runs with minimal overhead on verified Linux systems, including those powered by NVIDIA H200 GPUs.
| Category | Details |
|---|---|
| Product Name | Fixstars AIBooster |
| Developer | Fixstars Corporation |
| Launch Date | January 2025 (latest update: July 2025) |
| Core Functions | Performance Observability (PO), Performance Intelligence (PI) |
| Main Features | Real-time monitoring, GPU/CPU/memory profiling, cost & performance analysis |
| Optimization Tools | Bottleneck detection, auto acceleration, hyperparameter & CPU tuning |
| Platforms Supported | On-premises, AWS, Azure, GCP |
| Supported OS | Debian-based Linux (verified: Ubuntu 22.04 LTS) |
| GPU Compatibility | Works best with NVIDIA GPUs (verified: H200 architecture) |
| Performance Gains | Up to 2.5x speed improvement (e.g., Llama 3.1 70B), 19% GPU cost savings |
| Use Cases | Autonomous driving, cloud optimization, telecom, broadcasting |
| Integration | Bundled with Fixstars AIStation for private AI workloads |
| Pricing | PO: Free forever; PI: Free for 1 month, then ~$100/month/GPU (H100) |
| Data Privacy | No user-specific data collected; only general usage stats |
| Installation Requirements | Linux system, internet access for updates |
| Availability | Free download from Fixstars website; online demos available |
Fixstars AIBooster: Supercharging AI Workloads with Performance Engineering
In the rapidly evolving landscape of Artificial Intelligence, where complex models like Large Language Models (LLMs) and real-time inference are becoming ubiquitous, the demand for highly efficient computing resources, particularly GPUs, is at an all-time high. Companies face significant challenges in optimizing their AI infrastructure, often encountering underutilization and performance bottlenecks that lead to increased costs and slower development cycles.
Enter Fixstars AIBooster, a comprehensive performance engineering platform developed by Fixstars Corporation, a leader in software optimization solutions. Launched in January 2025 (with a free downloadable version becoming available in May 2025 and the latest July 2025 version offering enhanced features), AIBooster aims to address these challenges by providing real-time monitoring, visualization, and intelligent optimization of AI workloads running on GPU servers, whether on-premises or in the cloud.
The Core Components: Observability and Intelligence
Fixstars AIBooster is built around two primary functions:
- Performance Observability (PO): This module continuously monitors and saves detailed performance data from active AI workloads. It visualizes key metrics and trends over time through an intuitive dashboard, enabling users to clearly identify bottlenecks and performance issues. PO offers:
- Continuous Monitoring: Efficiently collects hardware and AI workload data as time-series.
- Unified Monitoring: Supports multiple platforms, including AWS, Azure, GCP, and on-premises environments, allowing seamless monitoring of diverse system architectures in one place.
- Detailed Metrics: Provides insights into:
- CPU usage
- GPU usage (including CUDA core utilization)
- Memory usage
- Storage usage
- Network usage
- Software profiling results (down to library and function levels, including continuous flame graph generation for visualizing processing time breakdown).
- Job-based Visualization: Manages AI workloads as “jobs” and visualizes performance metrics on a per-job basis.
- Lustre Support: Capability to collect and visualize metrics from Lustre, a distributed file system commonly used in large-scale cluster environments.
- Enhanced GPU Profiling: Complements flame graphs with detailed GPU profiling (using Perfetto in the July 2025 version) to quickly pinpoint performance bottlenecks.
- Cost Analysis View: The July 2025 version introduces a tailored dashboard view specifically for business leaders to visualize infrastructure operating costs.
- Performance Analysis View: A dedicated view for AI developers to delve into performance metrics.
- Performance Intelligence (PI): Leveraging the data collected by PO, this module provides a suite of tools for automatic acceleration and intelligent suggestions to optimize AI workloads. PI features:
- Bottleneck Identification: Analyzes performance data to pinpoint processing bottlenecks.
- Optimization Suggestions: Proposes improvements based on the identified issues.
- Automated Acceleration: Automates optimization processes to enhance GPU utilization and accelerate AI training and inference.
- Hyperparameter Tuning: Automatic collection and visualization of hyperparameter tuning results, significantly accelerating the identification of optimal parameters (July 2025 version).
- Framework-Specific Tuning: Automated tuning capabilities specifically designed for popular frameworks like MMEngine and DeepSpeed (July 2025 version).
- CPU Affinity Optimization: Automatic CPU Affinity optimization to further enhance performance (July 2025 version).
- Manual Acceleration Tools: Provides various analytical tools for engineers to manually accelerate their AI workloads for further performance improvements.
Key Benefits and Proven Impact
Fixstars AIBooster offers substantial advantages for organizations leveraging AI:
Accelerated AI Training and Inference: Actual project data shows processing speed improvements of up to x1.2 (though recent benchmarks on H200 GPUs indicate up to 2.5x acceleration for specific workloads like Llama 3.1 70B pre-training).
Significant Cost Savings: By maximizing GPU utilization and optimizing infrastructure efficiency, users can achieve up to 19% GPU cost savings (based on Fixstars’ actual projects).
Reduced Development Time: Faster identification and resolution of bottlenecks lead to quicker AI model training and deployment.
Continuous Optimization Cycle: Facilitates a performance engineering cycle where new models or methods are continuously monitored and optimized as computation patterns evolve.
Near-Zero Overhead: The software runs as a Linux daemon with minimal impact on system performance.
Architecture and Supported Environments
Fixstars AIBooster typically consists of two main components:
- AIBooster Agent: Installed on individual compute nodes, it collects performance telemetry data.
- AIBooster Server: Installed on a management node, it stores the collected data and provides visualizations via an intuitive dashboard.
The software runs on Debian-based Linux environments, with Ubuntu 22.04 LTS being verified for operation. An internet connection is generally required for installation and updates. While it can run without an NVIDIA GPU, data and functionality will be limited. Fixstars AIBooster has also been verified to function effectively and achieve acceleration on the latest NVIDIA H200 architecture GPUs.
Use Cases and Industry Adoption
Fixstars AIBooster has a growing list of proven use cases across various industries:
Autonomous Driving: Utilized by Sony Honda Mobility Inc. for its “AFEELA” mobility brand’s autonomous driving AI learning environment, contributing to improved machine learning speed and optimized hardware performance.
High-Efficiency GPU Infrastructure R&D: Employed in a joint R&D initiative between Optage Inc. and GMI Cloud for creating high-efficiency GPU infrastructure.
Telecommunications and Broadcasting: Adopted by companies in these sectors to enhance operational performance across varied AI workloads.
Cloud Environments: Seamlessly integrates with cloud platforms (AWS, Azure, GCP) for unified monitoring and optimization.
Private AI Utilization: Bundled with “Fixstars AIStation,” an all-in-one private AI workstation service, to optimize highly confidential tasks in local environments.
Pricing and Availability
- Performance Observability (PO) function: Permanently free to use.
- Performance Intelligence (PI) function: Free for the first month after activation. Thereafter, fees are incurred based on GPU usage (e.g., $100/month/GPU for an NVIDIA H100 equivalent). Pricing for other GPU models requires direct inquiry with Fixstars.
- Expert Support: Fixstars also offers performance engineering services where their experts provide assistance based on AIBooster’s analysis data.
Fixstars AIBooster is available for free download from the Fixstars website, and online demos are provided to showcase its functionality.
Data Privacy
Fixstars emphasizes that it does not collect user-specific data, such as application data or detailed analysis results. Only general usage statistics are gathered for product improvement purposes, ensuring customer data privacy.
The Future of AI Infrastructure Optimization
As AI models continue to grow in complexity and scale, tools like Fixstars AIBooster are becoming indispensable for organizations to manage escalating GPU infrastructure costs and ensure optimal performance. By providing comprehensive observability and intelligent optimization capabilities, AIBooster empowers businesses to maximize their computing resources, accelerate AI development, and maintain a competitive edge in the fast-paced world of artificial intelligence.
FAQs about Fixstars AIBooster
What is Fixstars AIBooster?
Fixstars AIBooster is a performance engineering platform that optimizes AI workloads through real-time monitoring and intelligent acceleration, helping users maximize GPU efficiency and reduce infrastructure costs.
Who developed AIBooster?
AIBooster was developed by Fixstars Corporation, a company known for software optimization and high-performance computing solutions.
When was Fixstars AIBooster launched?
It was officially launched in January 2025, with major feature updates released in May and July 2025.
What are the core components of AIBooster?
AIBooster consists of two main components: Performance Observability (PO) for real-time monitoring, and Performance Intelligence (PI) for automated and manual optimization of AI workloads.
What does the Performance Observability (PO) module do?
PO continuously monitors system and AI job performance metrics, including GPU, CPU, memory, network, and storage usage. It offers visual dashboards and profiling tools like flame graphs and GPU performance summaries.
What is Performance Intelligence (PI)?
PI uses data from PO to identify performance bottlenecks and provide intelligent suggestions for optimization, including automated GPU acceleration, hyperparameter tuning, and CPU affinity enhancements.
Is AIBooster compatible with cloud platforms?
Yes, it supports AWS, Azure, and Google Cloud Platform (GCP), in addition to on-premises environments.
Can AIBooster be used on any operating system?
It is designed for Debian-based Linux distributions and has been verified on Ubuntu 22.04 LTS.
Does AIBooster work without a GPU?
Yes, but functionality and data collection will be limited without an NVIDIA GPU.
What kind of performance improvements can users expect?
Depending on the workload, users can experience up to 2.5x acceleration (e.g., Llama 3.1 70B pre-training) and up to 19% GPU cost savings.
Does AIBooster support real-time inference optimization?
Yes, it is designed to monitor and accelerate both training and inference workloads in real time.
How is AIBooster installed?
It consists of an AIBooster Agent installed on compute nodes and an AIBooster Server on a management node. Installation requires an internet connection.
What visualization tools are included?
AIBooster includes dashboards for job-level views, flame graphs, GPU profiling with Perfetto, cost analysis, and detailed software profiling.
Can business leaders benefit from using AIBooster?
Yes, it includes a dedicated cost analysis dashboard to help business stakeholders track infrastructure expenses.
What are the licensing and pricing details?
The Performance Observability (PO) module is free to use permanently. Performance Intelligence (PI) is free for one month, after which pricing is based on GPU usage, starting at around $100/month per NVIDIA H100-equivalent GPU.
Is expert support available?
Yes, Fixstars offers performance engineering consulting based on AIBooster’s analysis for companies needing specialized help.
What industries use AIBooster?
Industries include autonomous driving (e.g., Sony Honda Mobility), telecommunications, broadcasting, and cloud infrastructure R&D.
Can AIBooster be used in confidential, private environments?
Yes, it is bundled with Fixstars AIStation for secure, on-premise AI workload optimization.
Does AIBooster collect any user data?
No, Fixstars does not collect application-specific or detailed analysis data. Only general usage statistics are gathered to improve the product.
What kind of workloads does AIBooster support?
It supports AI training, real-time inference, hyperparameter tuning, and optimization across popular frameworks like MMEngine and DeepSpeed.
Does AIBooster introduce system overhead?
No, it operates as a lightweight Linux daemon with near-zero overhead on system performance.
Can engineers manually accelerate workloads using AIBooster?
Yes, it offers a suite of manual analysis and optimization tools in addition to automated features.
Where can AIBooster be downloaded?
It is available for free download on the official Fixstars website.
Is a demo of AIBooster available?
Yes, Fixstars provides online demos to showcase the platform’s capabilities.
Does AIBooster support distributed file systems?
Yes, it supports Lustre, a distributed file system commonly used in large-scale cluster environments.
Is AIBooster compatible with the latest GPU hardware?
Yes, it has been verified to work effectively on NVIDIA H200 architecture GPUs.
What is the CPU affinity optimization feature?
It automatically optimizes CPU affinity to ensure better task scheduling and enhanced performance in AI workloads.
What is the hyperparameter tuning feature in AIBooster?
It automatically collects and visualizes tuning results to help identify optimal model parameters faster.
Is framework-specific optimization supported?
Yes, the platform includes automatic tuning tailored for frameworks like MMEngine and DeepSpeed.
Can AIBooster visualize performance per AI job?
Yes, it treats each workload as a “job” and provides detailed performance metrics and visualizations at the job level.
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