We're excited to share that Blocks and Files has published an interview with GridGain CTO Lalit Ahuja on the topic of GridGain’s applicability to AI inferencing.
Achieving real-time inferencing for predictions and insights demands ultra-low latency access to massive and often dynamic datasets. The GridGain Platform provides a distributed memory space across clusters, with the unique ability to unify feature stores, prediction caches, model repositories, and vector search. These capabilities simplify AI infrastructure and reduce data movement across the network, enabling AI models to access precise, up-to-the-second data for instant, accurate inferences.
In this way, GridGain both accelerates AI processing and streamlines its deployment – from experimentation to production, ensuring better business outcomes from AI investments.
Here are a few highlights from the interview:
On how GridGain can help with AI model training
The platform is often used to accelerate the training of AI models, including generation of test data for training purposes or for continuous training, where features can be extracted or vector embeddings generated in real time from incoming transactions and events and made available to the model training exercise, all within GridGain.
On how the platform is evolving to support LLM training and inferencing
GridGain has the ability to introduce recency into the LLM prompts and RAG applications by generating vector embeddings on the fly, writing them to its in-memory vector store, and making these vectors available to RAG applications.
On GridGain’s unique ability to process events and transactions
GridGain can process events and transactions, embellish them with historical, contextual data, extract features, generate vectors, and execute any AI workload on this curated data, all in the context of a transaction or an event-driven decision.
On GridGain as a “cluster of resources”
GridGain’s resources – servers, VMs, nodes, pods, etc. – can be deployed simultaneously on-premises, in any cloud or both together, with data distributed in memory across the cluster. This cluster can scale horizontally, within or across data centers, and it can be configured to maintain full data integrity with ACID compliance and zero data loss, with optional durable disk-based stores for backups, snapshots, and point-in-time recovery capabilities.
On GridGain data partitioning
Data is partitioned across the various memory resources within the cluster, with the option to replicate the data across the cluster for redundancy, high availability and configurable immediate/strict or eventual consistency. The resources in the cluster constantly communicate with each other, and data consistency and cluster organization is managed by robust industry-standard consensus protocols implemented within GridGain.
On GridGain for edge AI inferencing use cases
GridGain can run on edge infrastructure, with use cases in telco and IoT-related edge computing applications. It can run localized compute/analytics on relevant data delivered to or fed to the edge clusters from localized sensors, devices or event streams, or other connected GridGain clusters. Globally deployed GridGain clusters can selectively replicate data amongst each other, with the added ability to protect against network segmentation.
Read the full Blocks and Files article to learn more about how GridGain is redefining the data foundation for real-time AI.