You’ve got Questions.
We’ve got answers.
Common Inquiries
We understand that when questions naturally come up when exploring new platforms. That's why we've taken the time to put together answers to some of the most common inquiries we receive about Rapt. Our goal is to help you get a clearer picture of how our AI-driven platform can simplify your processes, optimize resources, and ultimately help you achieve better results—faster and more efficiently.
Take a look at the sections below to see if your questions are covered. If you don’t find what you’re looking for, don’t worry! Just send us a quick note. We're always here to help and are more than happy to make sure you have the information you need to make the best decision for your AI operations.
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Rapt platform works with AI model training, fine-tuning and inference. Rapt product analyzes and profiles model workloads when data scientists click go to train, fine-tune and deploy inference sessions. We automate all the infrastructure related tasks – configure, setup and tuning. In addition, it optimizes the GPU compute resources for model workstreams and deploys the model across your cloud VPCs, including hybrid and multi-cloud, on-premise infrastructure.
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We support industry-leading enterprise GPUs from Nvidia and AMD, along with accelerators such as Google TPUs and AWS Trainiums. When appropriate, we send workloads like ETL and data cleansing tasks to available CPUs, though we do not optimize workloads for CPUs.
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No. Those DSML platforms do a great job with AI workflow pipeline management, data preparation, and version management of training, fine-tuning and inference sessions.
In comparison, the Rapt platform automates AI infrastructure and AI compute. Rapt analyzes AI model workloads and optimizes the accelerated compute. In addition, the Rapt multi-layer scheduler auto-shares GPUs and distributes AI-workloads across GPUs. Users can run AI models on these DSML workflow platforms and plug Rapt into those platforms to automate compute resource management and compute efficiency.
We complement and partner with most of the DSML platform companies.
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Rapt is a no-code solution that significantly simplifies the workload for data scientists. It integrates seamlessly with Kubernetes, requiring no changes to the data scientists' model code or workflow.
Data scientists can continue running their models as they do now, without interruptions or negative impacts on their workflow. Rapt eliminates the need for multiple iterations to set up and fine-tune GPU infrastructure, freeing data scientists from tedious tasks. This enables them to increase productivity and accelerate the deployment of AI models into inference and production.
How Rapt Adds Value to Your Organization
Rapt is a game-changer for organizations that prioritize the leveraging AI as a competitive advantageAI development, and seek maximum productivity and increased ROI. The platform is perfectly aligned with the needs of the following roles:
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Senior Executives benefit from Rapt by enabling their company to do more data science faster, operationalize GPUs, and at lower costs and higher ROI. Rapt enables a significant competitive advantage for all corporations embracing AI.
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Without any change to the Data Scientists processes or code, Rapt ensures:
Data Scientists no longer have to "wait" for GPU allocation
Tedious infra-setup and tuning (which can take hours) is fully automated, saving Data Scientists significant time
Auto-sharing GPUs without any presets and no user intervention
Configurations are fully optimized for the requirements of each specific model and any SLA's selected. These optimizations dramatically improve performance by ~3X.
Lastly, Rapt constantly monitors all models that are running and dynamically feeds them the resources (vGPU, Memory, SM's...) as needed so models do not fail and the Data Scientist does not have to "baby sit" the model while it runs.
Data scientists no longer have to call IT to have GPUs allocated and/or say their model failed or is performing slowly and that they need more resources.
Rapt's platform deploys using a fully optimize configurations for each Data Scientist doing training, fine-tuning, and inference sessions. This typically allows companies to run 4X the number on AI workloads on existing GPU/AI compute infrastructure or at current spend levels.
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MLOps can see the exact GPU utilization and project requirements going forward, rely on Rapt's auto-provisioning of GPU shares based on specific AI workload requirements and SLAs. Rapt also auto-allocates GPU instance types (spot, on-demand and reserved) based on availability and SLAs (performance, cost, priorities, etc.).
In addition, if spot instances are preferred, Rapt makes them non-disruptive. Once revocation or preemption is sensed, Rapt automatically migrates the workload to other available instances in the same or other clouds, enabling the workload to continue and complete successfully.
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The Rapt platform presents a simple, single interface to any cloud, and AI compute resources that the customer have access to. Our automation, deploys the fully optimized configuration on the most ideal and lowest cost infrastructure at any and all clouds the customer has a relationship with. This enables customers to dramatically simplify the cloud deployments for all AI workloads and dramatically reduces resources required to 'scour' other clouds for the best GPU price performance.
TESTIMONIAL
"Your cloud GPU optimization more than pays for this product."