STAC Summit, 2 May 2024, London

• Gad Benram, CTO and Founder, TensorOps

The world is now awash in proprietary and open-source large language models (LLMs). These models present a wide range of tradeoffs between fitness for purpose (quality and responsiveness of output relative to the requirements of the use case) and total cost of operation. For example, larger models can be more accurate but usually cost more and are sometimes slower. Many solution-design patterns can improve responses and/or reduce cost, including automated prompt engineering, retrieval augmented generation (RAG), agentic workflows, fine tuning, and model quantization. Enterprises also have a choice between using SaaS LLMs, self-hosting LLMs in the cloud or self-hosting them on prem, each of which has its own tradeoff curves. Choosing the right approach for a given use case--let alone for a central enterprise AI utility--is a big challenge. In this session, Gad will provide an overview of the key solution design patterns and considerations for assessing the multi-dimensional cost implications of different deployment options.

• Prasad Shinde, Solutions Architect / Lead Specialist, KX

Getting large language models (LLMs) to provide accurate and relevant output is a well-known challenge. It’s even more daunting in the fast-moving landscape of capital markets. Trading and investment firms care deeply about the sequence of events and how information changes over time. The key to unleashing the power of GenAI for markets is to integrate source content into a timeseries framework and to enable LLMs to operate on the wealth of traditional financial timeseries that firms already amass. Through practical, high-impact use cases in alpha gen, trade execution, and risk, Prasad will argue for a new kind of retrieval augmented generation (RAG) that fully leverages the temporal properties of information.

• Euan Wielewski, Machine Learning Lead, NatWest
• Gad Benram, CTO and Founder, TensorOps
• Zerrin Diren, Head of Sales Engineering for Financial Services Industry, Google Cloud, UK and Ireland, Google
• Christopher Marsh-Bourdon, VP of Engineering, Oracle
• Marcus Vinicius Martinez, Industry Advisor, EMEA FSI, Microsoft

Most financial firms experimented with LLMs in 2023. Some have solutions in production, and those who don't are mostly planning to in 2024. Once an initial solution is in production, AI architects will face the consequence of their success: demand for more users and use cases. But scaling generative AI is more complicated than usual. With more use cases come broader model governance challenges. LLM instances can be in short supply, scattered geographically, and diverse in capability. And fixed or variable costs can be "eye watering" (as one hedge fund described their first bills for AI inference). Fortunately, every few days the research and vendor communities supply new models, software, hardware, managed services, and design patterns to tackle these problems. Which of these have the most promise and will be practical in 2024? And what kind of architectural frameworks are best in a world of such rapid change? Our panel of experts will dig into these questions and yours.

• Jack Gidding, CEO, Strategic Technology Analysis Center

Jack will provide a preliminary look into STAC-AI™, an LLM benchmark suite guided by the priorities of financial firms, which measures a full solution stack -- from the model to the metal™

• Jack Gidding, CEO, Strategic Technology Analysis Center

Jack will discuss the latest Council activities and test results relating to 1) derivatives risk computation and 2) deep time-series analytics.

• Matt Raso-Barnett, Senior Systems Engineer, DDN
• Chris Lacey, HPC & AI Architect, Hewlett Packard Enterprise
• Steve Keogh, Area VP Systems Engineering, EMEA Strategic Field Tech, Pure Storage

Banks and hedge funds require more compute, storage, and networking to meet increasing demands for trading and risk analytics. Data volumes continue to balloon, regulations require more simulations, and new market opportunities require new analytics. However, generative AI and cloud have famously become "gravity wells" for the IT industry, driving its product roadmaps. On the one hand, this may increase the options available for financial HPC and data-intensive workloads, driving down long-term costs. On the other hand, AI and hyperscaler architectures can differ in important ways from those of today's trading and risk analytics, which presents challenges. To what extent can the finance industry benefit from the new products coming forth? How much longevity is left in existing approaches? Are there opportunities (or even imperatives) for trading and investment firms to rethink how they design their applications and infrastructure? To kick off, there will be some brief presentations:

• Jack Gidding, CEO, Strategic Technology Analysis Center

Jack will discuss the latest Council activities and test results relating to 1) low-latency LSTM inference on market data and 2) network stacks in the cloud and on the ground.

• Mike Schonberg, Director of Market Data Technology, Quincy Data

Building a time-synchronization network spanning multiple data centers within a metropolitan area is a formidable challenge, particularly when the requirements include fault tolerance, nanosecond accuracy, and traceability to UTC(NIST). Quincy Data undertook this challenge, using White Rabbit in Chicago and New Jersey to synchronize across the major trading venues and using GNSS to connect these metros into a single clock domain. Come to hear Mike explain some of the problems Quincy encountered in design and implementation and how they overcame them.

• Michael O’Sullivan, Engineering Director, Cadence

Supporting 25G Ethernet can reduce the latency of FPGA or ASIC algorithms--but only if it is implemented well. Signal integrity, power, and thermal challenges exist all the way from the 25G IP, through the package, and across the PCB. In this talk, Michael will explore these challenges and present methods to analyze and resolve them so that you can achieve cool speed with 25G.

• Dr Steve Warr, Quantitative Technology Director, Qube Research & Technologies
• Richard Croucher, STAC Fellow, ex-Barclays
• John Courtney, Fintech Marketing Manager, AMD
• Louis Liu, CEO, Shengli Technologies

To stay in the game, trading firms must manage ever-growing data rates and keep their architectures competitive, whether it’s making software faster or hardware smarter. But mounting requirements for regulation, compliance, and cyber are straining resources. Meanwhile, finding well-trained talent is only getting harder. What are the best strategies to navigate these constraints? What are the best buy/hold/sell strategies for technologies across the spectrum, from FPGA and ASIC to private clouds? Where does it make sense to buy third-party logic today? Our panel of experts will weigh in.