Maximize Hardware Efficiency, Minimize Operational Costs.
Fixstars AI Booster optimizes hardware resource utilization and accelerates both training and inference processes, enabling cost-efficient AI development and operations. It supports a wide range of hardware environments and scales effortlessly from a single node to large-scale clusters.
Through continuous monitoring and acceleration, AI Booster accelerates AI training and
inference,
improving resource utilization and productivity.
Regularly sample system metrics to infer AI performance bottlenecks
Leverages monitoring feedback for continuous acceleration improvements
Continued use enables even greater acceleration over time
Identify relationships between program execution and hardware resource usage based on monitoring results.
Monitor hardware load
Identify resource-intensive processes
Detect Inefficient CodeExplore improvements and accelerate performance by identifying bottlenecks from monitoring results.
Replacing components with appropriate software libraries
Adjusting parameters effectively
Applying parallelization methods
By using monitoring results after the speed improvements as feedback, we can continuously iterate on the acceleration cycle.
Case Study: Accelerating Megatron-LM, a Continuous Pre-training Library for Enhancing LLMs with Specialized Knowledge
Learn more about our pre-training acceleration case study
Download Whitepaper
In fields demanding advanced computation and large-scale data processing, it fully harnesses GPU performance to significantly enhance development efficiency and productivity.
By increasing processing speed and cutting down training time, Fixstars AI Booster significantly reduces the cost per training run.
| Cloud A | Cloud B | Cloud A +Fixstars AI Booster |
Cloud B +Fixstars AI Booster |
|
|---|---|---|---|---|
| Node usage fee | $71,773 (On-demand) |
$20,000 （Monthly） |
$71,773 (On-demand) |
$20,000 （Monthly） |
| Processing performance | 1（Standard） | 1 | x2.5 | x2.5 |
| Usage period | 6 month | 6 month | 6 month | 6 month |
| Number of training runs possible within the period | 1.9 | 1.9 | 4.7 | 4.7 |
| Days per training run | 95 days | 95 days | 38 days | 38 days |
| Cost per training run | $454,066 | $126,600 | $181,600 | $50,666 |
We will accelerate your AI models and fine-tune Fixstars AI Booster to match your specific development environment and requirements.
We offer GPU server recommendations and setup support tailored to your needs and development environment.
We can propose various cloud services or on-premises servers equipped with NVIDIA’s high-end GPUs.
Modification is not needed. Built on standard open-source middleware used in generative AI/LLMs, it typically works with common code. For further performance improvement, adding arguments may be recommended.
Most likely. Our middleware integrated features realizing high efficiency based on the latest research papers. However, the speedup factor depends on your applications. Please consider our GPU workload analysis service for more details.
We optimized LLM middleware to the target servers. For instance, we tuned multi-GPU/multi-node communication and file storage for our cloud environment, resulting in 2.1x-3.7x performance boots.
Fixstars engineers acquired knowledge and skills to accelerate a large variety of computations including AI/ML from our engineering services. So we can apply acceleration techniques we have cultivated so far to this field.
