NVIDIA’s H100 GPUs & The AI Frenzy; a Rundown of Current Situation

We all are well aware of NVIDIA and the AI "gold mine" that has recently taken everyone by storm. In the midst of everything stands Team Green's H100 AI GPUs, which are simply the most sought-after piece of hardware for AI at the moment with everyone trying to get hands on one to power their AI needs.

NVIDIA H100 GPU Is The Best Chip For AI At The Moment & Everyone Wants More of Those

This article isn't particularly news but highlights readers on the current situation of the AI industry, and how companies are revolving around the H100 GPUs for their "future".

Before we go into the crux of the article, giving a recap becomes a necessity. So, at the start of 2022, everything was going fine with the usual developments. However, with November's arrival, a revolutionary application emerged named "ChatGPT", which established the foundations of the AI hype. While we cannot categorize "ChatGPT" as the founder of the AI boom, we certainly can say that it acted like a catalyst. With it emerged competitors like Microsoft and Google, getting forced into an AI race to release generative AI applications.

You might say, where does NVIDIA come in here? The backbone of generative AI involves hefty LLM (Large Language Model) training periods, and the NVIDIA AI GPUs come in clutch here. We won't go into tech specs and factual bits since that makes things dull and no fun to read. However, if are into getting to know specifics, we are dropping a table below, highlighting every AI GPU release from NVIDIA, dating back to Tesla models.

NVIDIA HPC / AI GPUs

NVIDIA Tesla Graphics Card	NVIDIA H100 (SMX5)	NVIDIA H100 (PCIe)	NVIDIA A100 (SXM4)	NVIDIA A100 (PCIe4)	Tesla V100S (PCIe)	Tesla V100 (SXM2)	Tesla P100 (SXM2)	Tesla P100 (PCI-Express)	Tesla M40 (PCI-Express)	Tesla K40 (PCI-Express)
GPU	GH100 (Hopper)	GH100 (Hopper)	GA100 (Ampere)	GA100 (Ampere)	GV100 (Volta)	GV100 (Volta)	GP100 (Pascal)	GP100 (Pascal)	GM200 (Maxwell)	GK110 (Kepler)
Process Node	4nm	4nm	7nm	7nm	12nm	12nm	16nm	16nm	28nm	28nm
Transistors	80 Billion	80 Billion	54.2 Billion	54.2 Billion	21.1 Billion	21.1 Billion	15.3 Billion	15.3 Billion	8 Billion	7.1 Billion
GPU Die Size	814mm2	814mm2	826mm2	826mm2	815mm2	815mm2	610 mm2	610 mm2	601 mm2	551 mm2
SMs	132	114	108	108	80	80	56	56	24	15
TPCs	66	57	54	54	40	40	28	28	24	15
FP32 CUDA Cores Per SM	128	128	64	64	64	64	64	64	128	192
FP64 CUDA Cores / SM	128	128	32	32	32	32	32	32	4	64
FP32 CUDA Cores	16896	14592	6912	6912	5120	5120	3584	3584	3072	2880
FP64 CUDA Cores	16896	14592	3456	3456	2560	2560	1792	1792	96	960
Tensor Cores	528	456	432	432	640	640	N/A	N/A	N/A	N/A
Texture Units	528	456	432	432	320	320	224	224	192	240
Boost Clock	TBD	TBD	1410 MHz	1410 MHz	1601 MHz	1530 MHz	1480 MHz	1329MHz	1114 MHz	875 MHz
TOPs (DNN/AI)	3958 TOPs	3200 TOPs	1248 TOPs 2496 TOPs with Sparsity	1248 TOPs 2496 TOPs with Sparsity	130 TOPs	125 TOPs	N/A	N/A	N/A	N/A
FP16 Compute	1979 TFLOPs	1600 TFLOPs	312 TFLOPs 624 TFLOPs with Sparsity	312 TFLOPs 624 TFLOPs with Sparsity	32.8 TFLOPs	30.4 TFLOPs	21.2 TFLOPs	18.7 TFLOPs	N/A	N/A
FP32 Compute	67 TFLOPs	800 TFLOPs	156 TFLOPs (19.5 TFLOPs standard)	156 TFLOPs (19.5 TFLOPs standard)	16.4 TFLOPs	15.7 TFLOPs	10.6 TFLOPs	10.0 TFLOPs	6.8 TFLOPs	5.04 TFLOPs
FP64 Compute	34 TFLOPs	48 TFLOPs	19.5 TFLOPs (9.7 TFLOPs standard)	19.5 TFLOPs (9.7 TFLOPs standard)	8.2 TFLOPs	7.80 TFLOPs	5.30 TFLOPs	4.7 TFLOPs	0.2 TFLOPs	1.68 TFLOPs
Memory Interface	5120-bit HBM3	5120-bit HBM2e	6144-bit HBM2e	6144-bit HBM2e	4096-bit HBM2	4096-bit HBM2	4096-bit HBM2	4096-bit HBM2	384-bit GDDR5	384-bit GDDR5
Memory Size	Up To 80 GB HBM3 @ 3.0 Gbps	Up To 80 GB HBM2e @ 2.0 Gbps	Up To 40 GB HBM2 @ 1.6 TB/s Up To 80 GB HBM2 @ 1.6 TB/s	Up To 40 GB HBM2 @ 1.6 TB/s Up To 80 GB HBM2 @ 2.0 TB/s	16 GB HBM2 @ 1134 GB/s	16 GB HBM2 @ 900 GB/s	16 GB HBM2 @ 732 GB/s	16 GB HBM2 @ 732 GB/s 12 GB HBM2 @ 549 GB/s	24 GB GDDR5 @ 288 GB/s	12 GB GDDR5 @ 288 GB/s
L2 Cache Size	51200 KB	51200 KB	40960 KB	40960 KB	6144 KB	6144 KB	4096 KB	4096 KB	3072 KB	1536 KB
TDP	700W	350W	400W	250W	250W	300W	300W	250W	250W	235W

The question still isn't answered here, why the H100s? Well, we are getting there. NVIDIA's H100 is the company's highest-end offering, providing immense computing capabilities. One might argue that the bump in performance brings in higher costing, but companies tend to order huge volumes, and "performance per watt" is the priority here. Compared to the A100, the Hopper "H100" brings in 3.5 times more 16-bit inference and 2.3 times 16-bit training performance, making it the obvious choice.

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So now, we hope the superiority of the H100 GPU is evident here. Now, moving on to our next segment, why is there a shortage? The answer to this involves several aspects, the first being the vast volumes of H100s needed to train a single model. An astonishing fact is that OpenAI's GPT-4 AI model required around 10,000 to 25,000 A100 GPUs (at that time, H100s weren't released).

Modern AI startups such as Inflection AI and CoreWeave have acquired humongous amounts to H100s, with a total worth accounting in billions of dollars. This shows that a single company requires huge volumes, even to train a basic-to-decent AI model, due to which the demand has been tremendous.

If you question NVIDIA's approach, one can say, "NVIDIA could increase production to cope with demand." Saying this is much easier than actually implementing it. Unlike gaming GPUs, NVIDIA AI GPUs require extensive processes, with most of the manufacturing assigned to the Taiwanese semiconductor behemoth TSMC. TSMC is the exclusive supplier of NVIDIA's AI GPU, leading all stages from wafer acquisition to advanced packaging.

H100 GPUs are based on TSMC's 4N process, a revamped version of the 5nm family. NVIDIA is the biggest customer for this process since Apple previously utilized it for its A15 bionic chipset, but A16 Bionic has replaced that. Of all of the relevant steps, the production of HBM memory is the most complicated since it involves sophisticated equipment currently utilized by a few manufacturers.

CoWos Packaging Utilized in NVIDIA's H100

HBM suppliers include SK Hynix, Micron, and Samsung while TSMC has limited its suppliers, and we are unaware of who they are. However, apart from HBM, TSMC also faces problems maintaining CoWoS (Chip-on-Wafer-on-Substrate) capacity, a 2.5D packaging process, and a crucial stage in developing H100s. TSMC can't match the demand from NVIDIA, due to which order backlogs have reached new heights, getting delayed to December.

So when people use the word GPU shortage, they're talking about a shortage of, or a backlog of, some component on the board, not the GPU itself. It's just limited worldwide manufacturing of these things... but we forecast what people want and what the world can build.

-Charlie Doyle, NVIDIA's DGX VP and GM (via Computerbase.de)

We have left out many specifics, but going into detail will deviate from our primary aim, which is to detail an average user about the situation. While for now, we don't believe the shortage could reduce and, in turn, is expected to increase. However, we could see a landscape shift here after AMD's decision to consolidate its position in the AI market.

DigiTimes reports that "TSMC seems to be particularly optimistic about demand for AMD’s upcoming Instinct MI300 series, saying it will be half of Nvidia's total output of CoWoS-packaged chips" It may distribute the workload across companies. Still, judging by Team Green's greedy policies in the past, something like this would require a severe offering from AMD.

Summing up our talk, NVIDIA's H100 GPUs are leading the AI hype to new heights, which is why this frenzy surrounds them. We aimed to wrap up our talk by giving readers a general idea of the whole scenario. Credits to GPU Utilis for the idea behind this article; make sure to look at their report too.

Written by Muhammad Zuhair

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