Performance Engineering Platform
Install on your GPU server and let it gather runtime data from your AI workloads. It identifies bottlenecks, automatically enhances performance, and gives you detailed insights to achieve even greater manual optimization.
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You can download the Quick Start Guide, case studies, and other resources here.
Whether in the cloud or on-premises, simply install Fixstars AI Booster on your GPU servers to gather detailed performance data from active AI workloads, visualizing bottlenecks clearly.
Use these insights to drive performance improvements, creating a continuous cycle of monitoring and optimization—accelerating AI training and inference while significantly reducing infrastructure costs.
Performance is not constant—it evolves due to new model adoption, parameter changes, and infrastructure updates. By continuously running the performance improvement cycle, you can prevent degradation and always achieve peak performance.
Note: These results include both automatic accelerations by Fixstars AI Booster and additional manual accelerates based on collected performance data.
Fixstars AI Booster (AI Booster) consists of two main components:
Typically, one AI Booster Server is installed on a management node, while multiple AI Booster Agents run on compute nodes. This configuration enables comprehensive monitoring of multiple nodes from a single management point, visualizing performance across your entire infrastructure on one unified dashboard.
You can centrally visualize server groups distributed across multiple locations, whether in a multi-cloud environment with multiple cloud vendors or a hybrid environment combining on-premise and cloud.
For simpler setups, AI Booster also supports a local configuration, where both Server and Agent run together on a single node.
Install both AI Booster Server and AI Booster Agent on a single GPU-equipped workstation or server. Connect a monitor and check performance information directly via the dashboard. This setup offers the quickest route when you want to "just try running it" on offline test machines or benchmarking systems. No network configuration is required.
Install both AI Booster Server and AI Booster Agent on a single GPU-equipped workstation or server.
Users can access the dashboard provided by the server through their personal PCs using a browser via TCP
port 3000.
This configuration is ideal for small-scale proof-of-concept (PoC) projects requiring dashboard viewing by
multiple users.
Install AI Booster Server on a dedicated management node, and install AI Booster Agent on each GPU
compute node.
Users can access the management node’s dashboard from their personal PCs via a browser through TCP port
3000.
This configuration is recommended for most GPU cluster server systems.
If there is no dedicated management node, select one GPU-equipped node and install both AI Booster Server
and its own AI Booster Agent. Install only the AI Booster Agent on the remaining GPU-equipped nodes.
Users can access the dashboard provided by the GPU node with AI Booster Server installed, via their
personal PCs through a browser using TCP port 3000.
The software runs as a Linux daemon, meaning it's always active with minimal overhead. We refer to it as having "near-zero overhead."
It runs on Debian-based Linux environments. We have verified operation on Ubuntu 22.04 LTS. It can also run without an NVIDIA GPU, but the available data and functionality will be limited.
Fixstars AI Booster is free to use. However, the Performance Intelligence (PI) feature is available at no cost for the first month after activation and becomes a paid feature thereafter. Please refer to the Fixstars AI Booster's End User License Agreement for details.
Fixstars does not collect user-specific data (such as your application data or detailed analysis results). We only gather general usage statistics for product improvement purposes. Contact us for more details.
Traditional tools (e.g., DataDog, NewRelic) show hardware utilization, but Fixstars AI Booster additionally captures detailed AI workload data. It analyzes this data to identify and resolve performance bottlenecks.
It optimizes performance by analyzing data from Performance Observability (PO). This includes changing infrastructure configurations, tuning parameters, and optimizing source code to maximize GPU utilization.
Profiling tools (like NVIDIA Nsight) capture "snapshots" triggered by specific commands. In contrast, AI Booster continuously captures detailed performance data, enabling historical analysis and identification of performance degradation. AI Booster’s automatic acceleration suggestions and implementations are unique features.
Yes. Because the underlying technology is broadly applicable, other AI or GPU-accelerated workloads can also benefit. The exact improvements depend on your specific workload—please contact us for details.
Detect hidden bottlenecks and automatically accelerate your AI workloads.
Achieve further acceleration manually by utilizing acquired performance data.
