Memory Machine™ AI
Surfing GPUs for continuous optimization
MemVerge presentations
at AI Field Day
Computing in the AI Era
In the new era of GPU-centric computing for AI, average GPU utilization is less than 15% for a third of wandb survey respondents and less than 50% for over half of respondents. That’s why efficient utilization of GPU clusters is crucial for Enterprises to maximize their return on investment.
GPU-Centric
Computing Model for AI
GPU Utilization
(wandb survey)
Memory Machine AI
Memory Machine for AI addresses challenges of the AI era and GPU utilization head-on. Designed specifically for AI training, inference, batch, and interactive workloads, the advanced software allows your workloads to surf GPU resources for continuous optimization.
Serving GPU on-demand, Memory Machine ensures your clusters are fully utilized, delivering GPU-as-a-Service for superior performance, security, user experience, and cost savings.
Memory Machine AI
Memory Machine for AI features a suite capabilities essential to IT pros and users involved in deploying cluster infrastructure and AI applications
Memory Machine AI in the Data Center
Memory Machine AI is software used in the data center for AI workflow orchestration, GPU scheduling, transparent checkpointing, and memory management.
Key Features & Benefits
GPU Surfing
Ensure uninterrupted job execution by transparently migrating user jobs to available hardware resources when the original GPUs become unavailable, maintaining continuous operation and maximizing resource efficiency.
Automatically Suspend and Resume Jobs Transparently
Seamlessly move user jobs across the AI Platform, safeguard against out-of-memory conditions, and prioritize critical tasks by automatically suspending and resuming lower-priority jobs, ensuring uninterrupted and efficient resource management.
Optimal GPU Utilization
Intelligent GPU sharing algorithms eliminate idle resources and maximize utilization.
Intuitive User Experience
Easy-to-use UI, CLI, and API for seamless workload management. The user interface provides proactive monitoring and optimization.
Intelligent Job Queueing & Scheduling
Optimizes user jobs for the available hardware by employing a variety of advanced scheduling policies and algorithms to ensure maximum efficiency and performance.
Flexible GPU Allocations
Optimize resource utilization by dynamically reallocating idle GPUs from other projects, ensuring efficient use of available hardware and minimizing downtime.
Optimized for NVIDIA GPUs
Leverage advanced NVidia GPU capabilities for superior performance and efficiency with tailored optimizations that maximize the potential of NVidia hardware in AI/ML workloads.
Granular Resource Assignment
Partition your infrastructure into specific departments and projects to ensure precise and optimal allocation of resources, maximizing efficiency and reducing waste.
Comprehensive Workload Support
Accommodate diverse user workloads, including Training, Inference, Interactive, and Distributed tasks, with an integrated application and model directory that stores existing and customized Docker images in a secure private repository for streamlined deployment and management.
Extensive Infrastructure Support
Seamlessly integrate with diverse infrastructure environments, including Kubernetes, LSF, SLURM, bare metal, and public cloud platforms such as AWS, GCP, Azure, and more, providing unparalleled flexibility and scalability for AI/ML workloads.
Cloud Bursting
Enable seamless scheduling of user jobs on public cloud resources, ensuring continuous operation and scalability without compromising performance.
MemVerge Presentations at AI Field Day 6
AI Field Days are events produced by The Futurum Group that connect independent technologists with the latest developments in the field from the companies that are developing and applying AI to IT infrastructure. On January 29 at the AI Field Day 6 event, MemVerge unveiled its strategy for the AI infra market and new Memory Machine AI software. Watch the videos to see the MemVerge vision for the AI infra market, an overview of Memory Machine AI, a deep-dive into the company’s transparent checkpointing technology, and the point-of-view of a customer.
Charles Fan
CEO and Co-founder, MemVerge
CEO and Co-founder Charles Fan provides an overview of large language models (LLMs), agentic AI applications, and workflows for AI workloads. He then delves into the influence of agentic AI on technology in the data center, with a particular focus on software for AI infrastructure.
Steve Scargall
Director of Product Management, MemVerge
Director of Product Management Steve Scargall introduces Memory Machine AI software from MemVerge. He explains how platform engineers, data scientists, developers & MLOps engineers, decision-makers & project leads can use the software to optimize GPU usage with strategic resource allocation, flexible GPU sharing, real-time observability & optimization, and priority management.
Bernie Wu
Vice-President of Strategic Partnerships, MemVerge
Vice-President of Strategic Partnerships Bernie Wu explains the limitations of current checkpointing technology for AI and how transparent checkpointing, in the form of a MMAI K8S operator, that can address the challenges by efficiently pausing and/or re-locating long-running GPU workloads without application changes or awareness.
Steve Yatko
CEO and Founder, Oktay Technology
CEO and Founder of Oktay Technology Steve Yatko talks about what enterprises are saying about their AI application and infra deployments.