This morning is an important one for AMD – perhaps the most important of the year. After almost a year and a half of build-up, and even longer for actual development, AMD is launching their next generation GPU/APU/AI accelerator family, the Instinct MI300 series. Based on AMD's new CDNA 3 architecture, and combining it with AMD's proven Zen 4 cores, AMD will be making a full-court press for the high-end GPU and accelerator market with their new product, aiming to lead in both big-metal HPC as well as the burgeoning market for generative AI training and inference.

Taking the stage for AMD's launch event will be AMD CEO Dr. LIsa Su, as well as a numerous AMD executives and ecosystem partners, to detail, at last, AMD's latest generation GPU architecture, and the many forms it will come in. With both the MI300X accelerator and MI300A APU, AMD is aiming to cover most of the accelerator market, whether clients just need a powerful GPU or a tightly-coupled GPU/CPU pairing.

The stakes for today's announcement are significant. The market for generative AI is all but hardware constrained at the moment, much to the benefit of (and profits for) AMD's rival NVIDIA. So AMD is hoping to capitalize on this moment to cut off a piece – perhaps a very big piece – of the market for generative AI accelerators. AMD has made breaking into the server space their highest priority over the last half-decade, and now, they believe, is their time to take a big piece of the server GPU market.

12:56PM EST - We're here in San Jose for AMD's final and most important launch event of the year: Advancing AI

12:57PM EST - Today AMD is making the eagerly anticipated launch of their next-generation MI300 series of accelerators

12:58PM EST - Including MI300A, their first chiplet-based server APU, and MI300X, their stab at the most powerful GPU/accelerator possible for the AI market

12:59PM EST - I'd say the event is being held in AMD's backyard, but since AMD sold their campus here in the bay area several years ago, this is more like NVIDIA's backyard. Which is fitting, given that AMD is looking to capture a piece of the highly profitable Generative AI market from NVIDIA

12:59PM EST - We're supposed to start at 10am local time here - so in another minute or so

12:59PM EST - And hey, here we go. Right on time

01:00PM EST - Starting with an opening trailer

01:00PM EST - (And joining me on this morning's live blog is the always-awesome Gavin Bonshor)

01:00PM EST - Advancing AI... together

01:01PM EST - And here's AMD's CEO, Dr. Lisa Su

01:01PM EST - Today "is all about AI"

01:01PM EST - And Lisa is diving right in

01:02PM EST - It's only been just a bit over a year since ChatGPT was launched. And it's turned the computing industry on its head rather quickly

01:02PM EST - AMD views AI as the single most transformative technology in the last 50 years

01:02PM EST - And with a rather quick adoption rate, despite being at the very beginning of the AI era

01:02PM EST - Lisa's listing off some of the use cases for AI

01:03PM EST - And the key to it? Generative AI. Which requires significant investments in infrastructure

01:03PM EST - (Which NVIDIA has captured the lion's share of thus far)

01:03PM EST - In 2023 AMD projected the CAGR for the AI market would be $350B by 2027

01:04PM EST - Now they think it's going to be $400B+ by 2027

01:04PM EST - A greater than 70% compound annual growth rate

01:04PM EST - AMD's AI strategy is centered around 3 big strategic priorities

01:05PM EST - A broad hardware portfolio, an open and proven software ecosystem, and partnerships to co-innovate with

01:05PM EST - (AMD has historically struggled with software in particular)

01:05PM EST - Now to products, starting with the cloud

01:06PM EST - Generative AI requires tens of thousands of accelerators at the high-end

01:06PM EST - The more compute, the better the model, the faster the answers

01:06PM EST - Launching today: AMD Instinct MI300X accelerator

01:06PM EST - "Highest performance accelerator in the world for generative AI"

01:07PM EST - CDNA 3 comes wiht a new compute engine, sparsity support, industry-leading memory bandwidth and capacity, etc

01:07PM EST - 3.4x more perf for BF16, 6.8x INT8 perf, 1.6x memory bandwidth

01:07PM EST - 153B transistors for MI300X

01:08PM EST - A dozen 5nm/6nm chiplets

01:08PM EST - 4 I/O Dies in the base layer

01:08PM EST - 256MB AMD Infinity Cache, Infinity Fabric Support, etc

01:08PM EST - 8 XCD compute dies stacked on top

01:08PM EST - 304 CDNA 3 compute units

01:08PM EST - Wired to the IODs via TSVs

01:09PM EST - And 8 stacks of HBM3 attached to the IODs, for 192GB of memory, 5.3 TB/second of bandwidth

01:09PM EST - And immediately jumping to the H100 comparisons

01:10PM EST - AMD has the advantage in memory capacity and bandwidth due to having more HBM stacks. And they think that's going to help carry them to victory over H100

01:10PM EST - AMD finds they have the performance advantage in FlashAttention-2 and Llama 2 70B. At the kernel level in TFLOPS

01:11PM EST - And how does MI300X scale?

01:11PM EST - Comparing a single 8 accelerator server

01:12PM EST - Bloom 176B (throughput) and Llama 2 70B (latency) inference performance.

01:12PM EST - And now AMD's first guest of many, Microsoft

01:13PM EST - MS CTO, Kevin Scott

01:14PM EST - Lisa is asking Kevin for his thoughts on where the industry is on this AI journey

01:15PM EST - Microsoft and AMD have been building the foundation for several years here

01:16PM EST - And MS will be offering MI300X Azure instances

01:16PM EST - MI300X VMs are available in preview today

01:17PM EST - (So MS apparently already has a meaningful quanity of the accelerators)

01:17PM EST - And that's MS. Back to Lisa

01:17PM EST - Now talking about the Instinct platform

01:18PM EST - Which is based on an OCP (OAM) hardware design

01:18PM EST - (No fancy name for the platform, unlike HGX)

01:18PM EST - So here's a whole 8-way MI300X board

01:18PM EST - Can be dropped into almost any OCP-compliant design

01:19PM EST - Making it easy to install MI300X

01:19PM EST - And making a point that AMD supports all of the same I/O and networking capabilities of the competition (but with better GPUs and memory, of course)

01:20PM EST - Customers are trying to maximize not just space, but capital expedetures and operational expedetures as well

01:20PM EST - On the OpEx side, more memory means being able to run either more models or bigger models

01:21PM EST - Which saves on CapEx expenses by buying fewer hardware units overall

01:21PM EST - And now for the next partner, Oracle. Karan Batta, the SVP of Oracle Cloud Infrastructure

01:22PM EST - Oracle is one of AMD's major cloud computing customers

01:23PM EST - Oracle will be supporting MI300X as part of their bare metal compute offerings

01:23PM EST - And MI300X in a generative AI service that is in the works

01:24PM EST - Now on stage: AMD President Victor Peng to talk about software progress

01:25PM EST - AMD's software stack is traditionally been their achilles heel, despite efforts to improve it. Peng's big project has been to finally get things in order

01:25PM EST - Including building a unified AI software stack

01:25PM EST - Today's focus is on ROCm, AMD's GPU software stack

01:26PM EST - AMD has firmly attached their horse to open source, which they consider a huge benefit

01:26PM EST - Improving ROCm support for Radeon GPUs continues

01:26PM EST - ROMc 6 shipping later this month

01:27PM EST - It's been optimized for generative AI, for MI300 and other hardware

01:27PM EST - "ROCm 6 delivers a quantum leap in performance and capability"

01:28PM EST - Software perf optimization example with LLMs

01:28PM EST - 2.6x from optimized libraries, 1.4x from HIP Graph, etc

01:28PM EST - This, combined with hardware changes, is how AMD is delivering 8x more GenAI perf on MI300X versus MI250 (with ROCm 5)

01:29PM EST - Recapping recent acquisitions as well, such as the nod AI compiler

01:30PM EST - And on the ecosystem level, AMD has an increasing number of partners

01:30PM EST - Hugging Face arguably being the most important, with 62K+ models up and running on AMD hardware

01:31PM EST - AMD GPUs will be supported in the OpenAI Triton 3.0 release

01:32PM EST - Now for more guests: Databricks, Essential AI, and Lamini

01:33PM EST - The four of them are having a short chat about the AI world and their experience with AMD

01:34PM EST - Talking about the development of major tools such as vLLM

01:34PM EST - Cost is a huge driver

01:36PM EST - It was very easy to incluide ROCm in Databricks' stack

01:36PM EST - Meanwhile Essential AI is taking a full stack approach

01:37PM EST - The ease of use of AMD's software was "very pleasant"

01:38PM EST - And finally, Lamini's CEO, who has a PhD in Generative AI

01:39PM EST - Customers get to fully own their models

01:39PM EST - Imbuing LLNs with real knowledge

01:39PM EST - Had an AMD cloud in production for over the past year on MI210s/MI250s

01:40PM EST - Lamini has reached software parity with CUDA

01:41PM EST - Many of the genAI tools available today are open source

01:41PM EST - Many of them can run on ROCm today

01:43PM EST - AMD's Instinct products are critical to supporting the future of business software

01:46PM EST - And that's the mini-roundtable

01:47PM EST - Summing up the last 6 months of work on software

01:47PM EST - ROCm 6 shipping soon

01:47PM EST - 62K models running today, and more coming soon

01:48PM EST - And that's a wrap for Victor Peng. Back to Lisa Su

01:49PM EST - And now for another guest spot: Meta

01:49PM EST - Ajit Mathews, Sr. Director of Engineering at Meta AI

01:50PM EST - Meta opened access to the Llama 2 model family in July

01:50PM EST - "An open approach leads to better and safer technology in the long-run"

01:51PM EST - Meta has been working with EPYC CPUs since 2019. And recently deployed Genoa at scale

01:51PM EST - But that partnership is much broader than CPUs

01:52PM EST - Been using the Instinct since 2020

01:53PM EST - And Meta is quite excited about MI300

01:53PM EST - Expanding their partnership to include Instinct in Facebook's datacenters

01:53PM EST - MI300X is one of their fastest design-to-deploy projects

01:54PM EST - And Meta is pleased with the optimizations done for ROCm

01:55PM EST - (All of these guests are here for a reason: AMD wants to demonstate that their platform is ready. That customers are using it today and are having success with it)

01:55PM EST - Now another guest: Dell

01:56PM EST - Arthur Lewer, President of Core Business Operations for the Global Infrastrucutre Solutions Group

01:56PM EST - (Buying NVIDIA is the safe bet; AMD wants to demonstrate that buying AMD isn't an unsafe bet)

01:57PM EST - Customers need a better solution than today's ecosystem

01:58PM EST - Dell is announcing an update to the Poweredge 9680 servers. Now offering them with MI300X accelerators

01:58PM EST - Up to 8 accelerators in a box

01:58PM EST - Helping customers consolidate LLM training to fewer boxes

01:59PM EST - Ready to quote and taking orders today

02:01PM EST - And that's Dell

02:02PM EST - And here's another guest: Supermicro (we've now pivoted from cloud to enterprise)

02:02PM EST - Charles Liang, Founder, President, and CEO of Supermicro

02:03PM EST - Supermicro is a very important AMD server partner

02:05PM EST - What does Supermicro have planned for MI300X?

02:05PM EST - 8U air cooled system, and 4U system with liquid cooling

02:05PM EST - Up to 100kW racks of the latter

02:05PM EST - And that's Supermicro

02:06PM EST - And another guest: Lenovo

02:06PM EST - Kirk Skaugen, President of Lenovo's Infrastructure Solutions Group

02:07PM EST - Lenovo believes that genAI will be a hybrid approach

02:07PM EST - And AI will be needed at the edge

02:08PM EST - 70 AI-ready server and infrastructure products

02:09PM EST - Lenovo also has an AI innovators program for key verticals for simplifying things for customers

02:10PM EST - Lenovo thinks inference will be the dominate AI workload. Training only needs to happen once; inference happens all the time

02:11PM EST - Lenovo is bring MI300X to their ThinkSystem platform

02:11PM EST - And available as a service

02:12PM EST - And that's Lenovo

02:13PM EST - And that's still just the tip of the iceberg for the number of partners AMD has lined up for Mi300X

02:13PM EST - And now back to AMD with Forrest Norrod to talk about networking

02:14PM EST - The compute required to train the most advanced models has increased by leaps and bounds over the last decade

02:14PM EST - Leading AI clusters are tens-of-thousands of GPUs, and that will only increase

02:14PM EST - So AMD has worked to scale things up on multiple fronts

02:14PM EST - Internally with Infinity Fabric

02:15PM EST - Near-linear scaling performance as you increase the number of GPUs

02:15PM EST - AMD is extending access to Infinity Fabric to innovators and strategic partners across the industry

02:15PM EST - We'll hear more about this initiative next year

02:16PM EST - Meanwhile the back-end network connecting the servers together is just as critical

02:16PM EST - And AMD believes that network needs to be open

02:17PM EST - And AMD is backing Ethernet (as opposed to InfiniBand)

02:17PM EST - And Ethernet is open

02:18PM EST - Now coming to the stage are a few netowrking leaders, including Arista, Broadcom, and Cisco

02:19PM EST - Having a panel discussion on Ethernet

02:21PM EST - What are the advantages of Ethernet for AI?

02:22PM EST - Majority of hyperscalers are using Ethernet or have a high desire to

02:23PM EST - The NIC is critical. People want choices

02:24PM EST - "We need to continue to innovate"

02:24PM EST - AI networks need to be open standards based. Customers need choices

02:25PM EST - Ultra Ethernet is a critical next step

02:26PM EST -

02:28PM EST - UEC is solving a very important technical problem of modern RDMA at scale

02:28PM EST - And that's the networking panel

02:28PM EST - Now on to high-performance computing (HPC)

02:29PM EST - Recapping AMD's experience thus far, including the most recent MI250X

02:29PM EST - MI250X + EPYC had a coherent memory space, but still the GPU and CPU separated by a somewhat slow link

02:29PM EST - But now MI300A is here with a unified memory system

02:29PM EST - Volume production began earlier this quarter

02:30PM EST - MI300 architecture, but with 3 Zen 4 CCDs layered on top of some of the IODs

02:31PM EST - 128GB of HBM3 memory, 4 IODs, 6 XCDs, 3 CCDs

02:31PM EST - And truly unified memory, as both GPU and CPU tiles go through the shared IODs

02:32PM EST - Performance comparisons with H100

02:32PM EST - 1.8x the FP64 and FP32 (vector?) performance

02:33PM EST - 4x performnace on OpenFOAM with MI300A versus H100

02:33PM EST - Most of the improvement comes from unified memory, avoiding having to copy around memory before it can be used

02:34PM EST - 2x the perf-per-watt than Grace Hopper (unclear by what metric)

02:35PM EST - MI300A will be in the El Capitan supercomputer. Over 2 EFLOPS of FP64 compute

02:35PM EST - Now rolling a video from HPE and the Lawrence Livermore National Lab

02:35PM EST - "El Capitan will be the most capable AI machine"

02:36PM EST - El Capitan will be 16x faster than LLNL's current supercomputer

02:37PM EST - And now another guest on stage: HPE

02:37PM EST - Trish Damkroger, SVP and Chief Product Officer

02:38PM EST - Frontier was great. El Capitan will be even better

02:39PM EST - AMD and HPE power a large number of the most power efficient supercomputers

02:40PM EST - (Poor Forrest is a bit tongue tied)

02:40PM EST - ElCap will have MI300A nodes with SlingShot fabric

02:41PM EST - One of the most capable AI systems in the world

02:41PM EST - Supercomputing is the foundation needed to run AI

02:42PM EST - And that's HPE

02:43PM EST - MI300A: A new level of high-performance leadership

02:43PM EST - MI300A systems avaialble soon from partners around the world

02:43PM EST - (So it sounds like MI300A is trailing MI300X by a bit)

02:43PM EST - Now back to Lisa

02:44PM EST - To cap off the day: Advancing AI PCs

02:44PM EST - AMD started including NPUs this year with the Ryzen Mobile 7000 series. The first x86 company to do so

02:44PM EST - Using AMD's XDNA architecture

02:45PM EST - A large computing array that is extremely performant and efficient

02:45PM EST - Shipped millions of NPU-enabled PCs this year

02:46PM EST - Showing off some of the software applications out there that offer AI acceleration

02:46PM EST - Adobe, Windows studio effects, etc

02:46PM EST - Announcing Ryzen AI 1.0 software for developers

02:46PM EST - So AMD's software SDK is finally available

02:47PM EST - Deploy tained and quantized models using ONNX

02:47PM EST - Announcing Ryzen Mobile 8040 series processors

02:47PM EST - Hawk Point

02:47PM EST - This is (still) the Phoenix die

02:48PM EST - With one wrinkle: faster AI performance thanks to a higher clocked NPU

02:48PM EST - AMD's own perf benchmarks show 1.4x over 7040 series

02:48PM EST - Now time for another guest: Microsoft

02:49PM EST - Pavan Davuluri, CVP for Windows and Devices

02:49PM EST - Talking about the work AMD and MS are doing together for client AI

02:50PM EST - Microsoft's marquee project is Copilot

02:52PM EST - MS wants to be able to load-shift between the cloud and the client. Seamless computing between the two

02:52PM EST - Showing AMD's NPU roadmap

02:53PM EST - Next-gen Strix Point processors in the works. Using a new NPU based on XDNA 2

02:53PM EST - Launching in 2024

02:53PM EST - XDNA 2 designed for "leadership" AI performance

02:53PM EST - AMD has silicon. So does MS

02:54PM EST - More than 3x the genAI perf (versus Hawk Point?)

02:55PM EST - And that's AI on the PC

02:55PM EST - Now recapping today's announcements

02:55PM EST - MI300X, shipping today. MI300A, in volume production

02:55PM EST - Ryzen Mobile 8040 Series, shipping now

02:56PM EST - "Today is an incredibly proud moment for AMD"

02:57PM EST - And that's it for Lisa, and for today's presentation

02:58PM EST - Thanks for joining us, and be sure to check out our expanded coverage of AMD's announcements

02:58PM EST -

02:58PM EST -

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  • snakeeater3301 - Wednesday, December 6, 2023 - link

    2x the HPC perf-per-watt than Grace Hopper (unclear by what metric) sneaky benchmark since Grace is a monstrous 72 core ARM V2 CPU and Nvidia always uses the ultimate hardline figures for their 700 watt TDP since its simultaneously using all the FP64 cores, FP32 cores and all the tensor cores (almost no application does that). MI300A has 308 CU which do the rated teraflops FP64 performance and the exactly equivalent rated teraflops FP32 performance means higher rated FP64 performance at the cost of lower rated FP32 performance and the FP64 cores in H100's implementation take a lot less space area.

    Also 72 V2 cores vs 24 Zen3 cores obviously the V2s are going to consume more power a heck a lot of power then MI300A's 24 Zwn3 cores.
  • mdriftmeyer - Wednesday, December 6, 2023 - link

    MI300A are Zen4C Chiplets.
  • Ryan Smith - Thursday, December 7, 2023 - link

    No. They are standard Zen 4 CCDs. 8 core CCDs, 3 CCDs in total.

    They are certainly not clocked as high as they can go, though. Only 3.7GHz or so.
  • mdriftmeyer - Thursday, December 7, 2023 - link

    Thanks for the correction. I just read the white paper. But as correctness goes, I'm far more accurate than the original poster, yet you didn't note that.
  • Dante Verizon - Wednesday, December 6, 2023 - link

    More cores generally bring greater energy efficiency.
  • mode_13h - Thursday, December 7, 2023 - link

    > Nvidia always uses the ultimate hardline figures for their 700 watt TDP

    Datacenter CPUs and GPUs actually stay in their power envelope, no matter whose they are. This is accomplished by clock-throttling, as necessary.

    > simultaneously using all the FP64 cores, FP32 cores and all the tensor cores

    Highly doubtful, since the fp64 "cores" are probably bolt-ons to the fp32 ones and they almost certainly share the same vector registers.

    > MI300A has 308 CU which do the rated teraflops FP64 performance and the exactly
    > equivalent rated teraflops FP32 performance means higher rated FP64 performance
    > at the cost of lower rated FP32 performance

    What's odd is that this was true of MI200, per my understanding, but AMD has a slide showing the MI300 has 2x the vector fp32 TFLOPS of its vector fp64 TFLOPS. It's only the tensor operations where they're the same. However, that's basically a non-issue, since AI workloads would use lower-precision types like tf32, fp16/bf16, or 8-bit, where MI300 offers even more performance (4x, 8x, and 16x, respectively).
  • fahadse0 - Monday, December 11, 2023 - link

    The scalability of MFT systems is not only a technical advantage but also a cost-effective solution. Organizations can scale their how to send video through email file transfer capabilities in alignment with business growth, avoiding unnecessary expenditures on extensive infrastructure upgrades.
  • mode_13h - Tuesday, December 12, 2023 - link

    This seems like search engine optimization spam.

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