AI Vendors - MemCon | Kisaco Research

AI Vendors - MemCon

Memory Con
March 2025
Silicon Valley, CA

Why Should AI Vendors Attend MemCon 2024?

We attract AI vendors from the likes of Aerospike, OpenAI, Microsoft Azure, Shopify, RunAI and more as they come together to:

  • Forge partnerships with systems vendors and technology partners.

  • Connect with cloud-vendors, end-user enterprises (banks, pharmas etc.) and investors.

  • Learn the implentations behind emerging technology.

If you'd like to find out more information about attending as an AI vendors, register your interest here

CONFIRM YOUR PLACE HERE

Featured Speakers Include

Author:

Zaid Kahn

VP & GM, Cloud AI & Advanced Systems
Microsoft

Zaid is currently GM in Cloud Hardware Infrastructure Engineering where he leads a team focusing on advanced architecture and engineering efforts for AI. He is passionate about building balanced teams of artists and soldiers that solve incredibly difficult problems at scale.

Prior to Microsoft Zaid was head of infrastructure engineering at LinkedIn responsible for all aspects of engineering for Datacenters, Compute, Networking, Storage and Hardware. He also lead several software development teams spanning from BMC, network operating systems, server and network fleet automation to SDN efforts inside the datacenter and global backbone including edge. He introduced the concept of disaggregation inside LinkedIn and pioneered JDM with multiple vendors through key initiatives like OpenSwitch, Open19 essentially controlling destiny for hardware development at LinkedIn. During his 9 year tenure at LinkedIn his team scaled network and systems 150X, members from 50M to 675M, and hiring someone every 7 seconds on the LinkedIn Platform.

Prior to LinkedIn Zaid was Network Architect at WebEx responsible for building the MediaTone network and later I built a startup that built a pattern recognition security chip using NPU/FPGA. Zaid holds several patents in networking and SDN and is also a recognized industry leader. He previously served as a board member of the Open19 Foundation and San Francisco chapter of Internet Society. Currently he serves on DE-CIX and Pensando advisory boards.

... read more

Zaid Kahn

VP & GM, Cloud AI & Advanced Systems
Microsoft

Zaid is currently GM in Cloud Hardware Infrastructure Engineering where he leads a team focusing on advanced architecture and engineering efforts for AI. He is passionate about building balanced teams of artists and soldiers that solve incredibly difficult problems at scale.

Prior to Microsoft Zaid was head of infrastructure engineering at LinkedIn responsible for all aspects of engineering for Datacenters, Compute, Networking, Storage and Hardware. He also lead several software development teams spanning from BMC, network operating systems, server and network fleet automation to SDN efforts inside the datacenter and global backbone including edge. He introduced the concept of disaggregation inside LinkedIn and pioneered JDM with multiple vendors through key initiatives like OpenSwitch, Open19 essentially controlling destiny for hardware development at LinkedIn. During his 9 year tenure at LinkedIn his team scaled network and systems 150X, members from 50M to 675M, and hiring someone every 7 seconds on the LinkedIn Platform.

Prior to LinkedIn Zaid was Network Architect at WebEx responsible for building the MediaTone network and later I built a startup that built a pattern recognition security chip using NPU/FPGA. Zaid holds several patents in networking and SDN and is also a recognized industry leader. He previously served as a board member of the Open19 Foundation and San Francisco chapter of Internet Society. Currently he serves on DE-CIX and Pensando advisory boards.

Author:

Petr Lapukhov

Network Engineer
NVIDIA

Petr Lapukhov is a Network Engineer at Meta. He has 20+ years in the networking industry, designing and operating large scale networks. He has a depth of experience in developing and operating software for network control and monitoring. His past experience includes CCIE/CCDE training and UNIX system administration.

... read more

Petr Lapukhov

Network Engineer
NVIDIA

Petr Lapukhov is a Network Engineer at Meta. He has 20+ years in the networking industry, designing and operating large scale networks. He has a depth of experience in developing and operating software for network control and monitoring. His past experience includes CCIE/CCDE training and UNIX system administration.

Author:

Sandeep Singh

Director - Applied DL & Computer Vision
Beans.ai
... read more

Sandeep Singh

Director - Applied DL & Computer Vision
Beans.ai

VIEW FULL SPEAKER LINE-UP

Agenda Highlights

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Opening Keynote: How Data and Workloads are Changing the Design of Systems, Clusters and Datacenters

Author:

Zaid Kahn

VP & GM, Cloud AI & Advanced Systems
Microsoft

Zaid is currently GM in Cloud Hardware Infrastructure Engineering where he leads a team focusing on advanced architecture and engineering efforts for AI. He is passionate about building balanced teams of artists and soldiers that solve incredibly difficult problems at scale.

Prior to Microsoft Zaid was head of infrastructure engineering at LinkedIn responsible for all aspects of engineering for Datacenters, Compute, Networking, Storage and Hardware. He also lead several software development teams spanning from BMC, network operating systems, server and network fleet automation to SDN efforts inside the datacenter and global backbone including edge. He introduced the concept of disaggregation inside LinkedIn and pioneered JDM with multiple vendors through key initiatives like OpenSwitch, Open19 essentially controlling destiny for hardware development at LinkedIn. During his 9 year tenure at LinkedIn his team scaled network and systems 150X, members from 50M to 675M, and hiring someone every 7 seconds on the LinkedIn Platform.

Prior to LinkedIn Zaid was Network Architect at WebEx responsible for building the MediaTone network and later I built a startup that built a pattern recognition security chip using NPU/FPGA. Zaid holds several patents in networking and SDN and is also a recognized industry leader. He previously served as a board member of the Open19 Foundation and San Francisco chapter of Internet Society. Currently he serves on DE-CIX and Pensando advisory boards.

... read more

Zaid Kahn

VP & GM, Cloud AI & Advanced Systems
Microsoft

Zaid is currently GM in Cloud Hardware Infrastructure Engineering where he leads a team focusing on advanced architecture and engineering efforts for AI. He is passionate about building balanced teams of artists and soldiers that solve incredibly difficult problems at scale.

Prior to Microsoft Zaid was head of infrastructure engineering at LinkedIn responsible for all aspects of engineering for Datacenters, Compute, Networking, Storage and Hardware. He also lead several software development teams spanning from BMC, network operating systems, server and network fleet automation to SDN efforts inside the datacenter and global backbone including edge. He introduced the concept of disaggregation inside LinkedIn and pioneered JDM with multiple vendors through key initiatives like OpenSwitch, Open19 essentially controlling destiny for hardware development at LinkedIn. During his 9 year tenure at LinkedIn his team scaled network and systems 150X, members from 50M to 675M, and hiring someone every 7 seconds on the LinkedIn Platform.

Prior to LinkedIn Zaid was Network Architect at WebEx responsible for building the MediaTone network and later I built a startup that built a pattern recognition security chip using NPU/FPGA. Zaid holds several patents in networking and SDN and is also a recognized industry leader. He previously served as a board member of the Open19 Foundation and San Francisco chapter of Internet Society. Currently he serves on DE-CIX and Pensando advisory boards.

Memory Optimizations for Large Language Models: From Training to Inference

Large Language Models (LLMs) have revolutionized natural language processing but have posed significant challenges in training and inference due to their enormous memory requirements. In this talk, we delve into techniques and optimizations to mitigate memory constraints across the entire lifecycle of LLMs.

The first segment explores Memory Optimized LLM Training. We discuss Training challenges and cover different techniques under Parameter Efficient Fine Tuning (PEFT). like prompt tuning with LoRA, and adapters.

LLMs inference is more memory bound rather than compute bound, In this section we will explore inference optimizations mostly for transformer architectures like Paged Key-Value (KV) Cache, Speculative Decoding, Quantization, Inflight Batching strategies, Flash Attention, each contributing to enhanced inference speed and efficiency.

Finally, we explore the concept of Coherent Memory, and how it helps with Inference optimizations by KV Cache offloading and LoRA weight re-computation.

By illuminating these advancements, this talk aims to provide a comprehensive understanding of state-of-the-art memory optimization techniques for LLMs, empowering practitioners to push the boundaries of natural language processing further.

Author:

Arun Raman

Deep Learning Solutions Architect
NVIDIA

Arun Raman is an AI solution architect at NVIDIA, adept at navigating the intricate challenges of deploying AI applications across edge, cloud, and on-premises environments within the consumer Internet industry. In his current role, he works on the design of end-to-end accelerated AI pipelines, for consumer internet customers meticulously addressing preprocessing, training, and inference optimizations.  His experience extends beyond AI, having worked with distributed systems and multi-cloud infrastructure. He shares practical strategies and real-world experiences, empowering organizations to leverage AI effectively.

... read more

Arun Raman

Deep Learning Solutions Architect
NVIDIA

Arun Raman is an AI solution architect at NVIDIA, adept at navigating the intricate challenges of deploying AI applications across edge, cloud, and on-premises environments within the consumer Internet industry. In his current role, he works on the design of end-to-end accelerated AI pipelines, for consumer internet customers meticulously addressing preprocessing, training, and inference optimizations.  His experience extends beyond AI, having worked with distributed systems and multi-cloud infrastructure. He shares practical strategies and real-world experiences, empowering organizations to leverage AI effectively.

How Deep Learning & Computer Vision Infrastructure Requires Application-Specific Infrastructure

Author:

Sandeep Singh

Director - Applied DL & Computer Vision
Beans.ai
... read more

Sandeep Singh

Director - Applied DL & Computer Vision
Beans.ai

VIEW THE FULL AGENDA