Introduction to Asynchronous Batching
Asynchronous batching is a technique used to improve the efficiency of AI workloads by allowing CPU batch preparation and GPU batch compute to run in parallel. This approach is crucial in reducing the idle gaps that occur when the CPU prepares the next batch while the GPU waits. In a loop running hundreds of steps per second, these idle gaps can account for nearly a quarter of total runtime.
The traditional synchronous approach to batching can lead to significant waste, particularly when using expensive GPU resources. For instance, using a GPU for an hour may be cost-effective, but using it for a day can result in substantial costs. Asynchronous batching addresses this issue by ensuring that both the CPU and GPU are always productive.
Asynchronous batching can be achieved through the use of CUDA streams, events, and non-blocking transfers. CUDA streams allow for concurrent execution of multiple kernels, while events enable synchronization between different streams. Non-blocking transfers enable data to be transferred between the CPU and GPU without blocking the CPU.
Serverless architectures are also proving invaluable in AI/ML use cases, enabling real-time processing and intelligent automation. By leveraging serverless architectures, businesses can build scalable and secure AI applications that can handle large volumes of data.
Technical Deep Dive: Architecting Production-Ready Data & AI Apps
Organizations are moving beyond simple dashboards to interactive, secure Data & AI applications built directly where their data lives. This session provides a technical deep dive into Databricks Apps, exploring how to transform complex AI logic into production-ready business tools.
We will cover the essential architectural choices for developers, including a comparison of Pythonic frameworks like Streamlit and Gradio versus full-stack JS/TS implementations. The discussion will focus on the end-to-end development lifecycle, teaching you how to master authentication via Service Principals and On-Behalf-Of (OBO) tokens, and how to implement robust production workflows using Databricks Asset Bundles (DABs).
Additionally, we will share best practices for optimizing performance and scalability—such as using async operations—and ensuring enterprise-grade observability through diagnostic logging and audit trails.
Clients should be allowed to upload arbitrarily large datasets; the system — not the client — must handle chunking and processing. This approach enables businesses to build scalable AI applications that can handle large volumes of data.
The guide will help you evaluate the right approach for your asynchronous processing requirements on the Salesforce platform. It explains each approach, providing you with the necessary knowledge to make informed decisions about your AI operations.
Unlocking Advanced GPU Architectures
To turn advanced GPU architectures into an operational AI factory that is scalable, schedulable, and easy to manage, businesses can leverage NVLink. NVLink is a high-speed interconnect that enables the transfer of data between GPUs and other components.
By leveraging NVLink, businesses can build scalable AI applications that can handle large volumes of data. This approach enables the creation of an operational AI factory that can manage multiple AI workloads concurrently.
Asynchronous batching is a crucial component of this approach, as it enables the efficient processing of AI workloads. By disentangling CPU batch preparation and GPU batch compute, businesses can ensure that both components are always productive.
The use of asynchronous batching and NVLink can significantly improve the efficiency of AI workloads, reducing costs and increasing productivity. This approach is essential for businesses that require scalable and secure AI applications.
By leveraging these technologies, businesses can build AI applications that can handle large volumes of data and provide real-time processing and intelligent automation.
Best Practices for Asynchronous Batching
To implement asynchronous batching effectively, businesses should follow best practices for optimizing performance and scalability. This includes using async operations, diagnostic logging, and audit trails.
Additionally, businesses should ensure that their AI applications can handle large volumes of data by allowing clients to upload arbitrarily large datasets. The system should handle chunking and processing, enabling the creation of scalable AI applications.
By following these best practices, businesses can ensure that their AI applications are efficient, scalable, and secure. This approach is essential for businesses that require real-time processing and intelligent automation.
Asynchronous batching is a crucial component of this approach, as it enables the efficient processing of AI workloads. By disentangling CPU batch preparation and GPU batch compute, businesses can ensure that both components are always productive.
The use of asynchronous batching and best practices can significantly improve the efficiency of AI workloads, reducing costs and increasing productivity. This approach is essential for businesses that require scalable and secure AI applications.
25%
reduction in idle gaps
30%
increase in productivity
40%
reduction in costs
Asynchronous Batching Comparison
Asynchronous Batching Comparison
| Component | Open / This Approach | Proprietary Alternative |
|---|---|---|
| Batching Approach | Asynchronous Batching | Synchronous Batching |
| GPU Utilization | Always Productive | Idle Gaps |
| CPU Utilization | Always Productive | Idle Gaps |
🔑 Key Takeaway
Asynchronous batching can significantly improve the efficiency of AI workloads by reducing idle gaps and increasing productivity. By disentangling CPU batch preparation and GPU batch compute, businesses can ensure that both components are always productive, reducing costs and increasing efficiency.
Key Links