A deep know-how convention for processor and system architects from trade and academia has change into a key discussion board for the trillion-dollar knowledge middle computing market.
At Sizzling Chips 2024 subsequent week, senior NVIDIA engineers will current the newest developments powering the NVIDIA Blackwell platform, plus analysis on liquid cooling for knowledge facilities and AI brokers for chip design.
They’ll share how:
- NVIDIA Blackwell brings collectively a number of chips, programs and NVIDIA CUDA software program to energy the subsequent era of AI throughout use circumstances, industries and international locations.
- NVIDIA GB200 NVL72 — a multi-node, liquid-cooled, rack-scale answer that connects 72 Blackwell GPUs and 36 Grace CPUs — raises the bar for AI system design.
- NVLink interconnect know-how supplies all-to-all GPU communication, enabling document excessive throughput and low-latency inference for generative AI.
- The NVIDIA Quasar Quantization System pushes the boundaries of physics to speed up AI computing.
- NVIDIA researchers are constructing AI fashions that assist construct processors for AI.
An NVIDIA Blackwell discuss, happening Monday, Aug. 26, may even highlight new architectural particulars and examples of generative AI fashions working on Blackwell silicon.
It’s preceded by three tutorials on Sunday, Aug. 25, that can cowl how hybrid liquid-cooling options might help knowledge facilities transition to extra energy-efficient infrastructure and the way AI fashions, together with massive language mannequin (LLM)-powered brokers, might help engineers design the subsequent era of processors.
Collectively, these shows showcase the methods NVIDIA engineers are innovating throughout each space of information middle computing and design to ship unprecedented efficiency, effectivity and optimization.
Be Prepared for Blackwell
NVIDIA Blackwell is the last word full-stack computing problem. It contains a number of NVIDIA chips, together with the Blackwell GPU, Grace CPU, BlueField knowledge processing unit, ConnectX community interface card, NVLink Swap, Spectrum Ethernet swap and Quantum InfiniBand swap.
Ajay Tirumala and Raymond Wong, administrators of structure at NVIDIA, will present a primary have a look at the platform and clarify how these applied sciences work collectively to ship a brand new commonplace for AI and accelerated computing efficiency whereas advancing vitality effectivity.
The multi-node NVIDIA GB200 NVL72 answer is an ideal instance. LLM inference requires low-latency, high-throughput token era. GB200 NVL72 acts as a unified system to ship as much as 30x quicker inference for LLM workloads, unlocking the power to run trillion-parameter fashions in actual time.
Tirumala and Wong may even focus on how the NVIDIA Quasar Quantization System — which brings collectively algorithmic improvements, NVIDIA software program libraries and instruments, and Blackwell’s second-generation Transformer Engine — helps excessive accuracy on low-precision fashions, highlighting examples utilizing LLMs and visible generative AI.
Retaining Information Facilities Cool
The normal hum of air-cooled knowledge facilities could change into a relic of the previous as researchers develop extra environment friendly and sustainable options that use hybrid cooling, a mix of air and liquid cooling.
Liquid-cooling strategies transfer warmth away from programs extra effectively than air, making it simpler for computing programs to remain cool even whereas processing massive workloads. The gear for liquid cooling additionally takes up much less area and consumes much less energy than air-cooling programs, permitting knowledge facilities so as to add extra server racks — and due to this fact extra compute energy — of their amenities.
Ali Heydari, director of information middle cooling and infrastructure at NVIDIA, will current a number of designs for hybrid-cooled knowledge facilities.
Some designs retrofit current air-cooled knowledge facilities with liquid-cooling models, providing a fast and simple answer so as to add liquid-cooling capabilities to current racks. Different designs require the set up of piping for direct-to-chip liquid cooling utilizing cooling distribution models or by fully submerging servers in immersion cooling tanks. Though these choices demand a bigger upfront funding, they result in substantial financial savings in each vitality consumption and operational prices.
Heydari may even share his workforce’s work as a part of COOLERCHIPS, a U.S. Division of Vitality program to develop superior knowledge middle cooling applied sciences. As a part of the undertaking, the workforce is utilizing the NVIDIA Omniverse platform to create physics-informed digital twins that can assist them mannequin vitality consumption and cooling effectivity to optimize their knowledge middle designs.
AI Brokers Chip In for Processor Design
Semiconductor design is a mammoth problem at microscopic scale. Engineers growing cutting-edge processors work to suit as a lot computing energy as they will onto a chunk of silicon a number of inches throughout, testing the boundaries of what’s bodily doable.
AI fashions are supporting their work by bettering design high quality and productiveness, boosting the effectivity of guide processes and automating some time-consuming duties. The fashions embody prediction and optimization instruments to assist engineers quickly analyze and enhance designs, in addition to LLMs that may help engineers with answering questions, producing code, debugging design issues and extra.
Mark Ren, director of design automation analysis at NVIDIA, will present an outline of those fashions and their makes use of in a tutorial. In a second session, he’ll give attention to agent-based AI programs for chip design.
AI brokers powered by LLMs could be directed to finish duties autonomously, unlocking broad functions throughout industries. In microprocessor design, NVIDIA researchers are growing agent-based programs that may purpose and take motion utilizing personalized circuit design instruments, work together with skilled designers, and study from a database of human and agent experiences.
NVIDIA consultants aren’t simply constructing this know-how — they’re utilizing it. Ren will share examples of how engineers can use AI brokers for timing report evaluation, cell cluster optimization processes and code era. The cell cluster optimization work not too long ago gained finest paper on the first IEEE Worldwide Workshop on LLM-Aided Design.
Register for Sizzling Chips, happening Aug. 25-27, at Stanford College and on-line.
