Fucking comma's in the data that are not the delimiter are annoying. Hate using stupid work arounds.
Yes, I am aware of double quotes and other work arounds like using different delimiters. Just thinking it would be easier to have one standard going forward.
I recently inherited a car which I don't need. If I decide to buy a new car how should I negotiate trading the two vehicles in. I have a 2021 Hyundai Tucson with about 45,000 miles and a 2011 Ford Escape with about 85,000 miles. Both are paid off. I would also not finance the remaining balance if there is one on the new car.
HBM is High Bandwidth Memory and what is being used in the AI chips. HBM sits next to the GPU. Currently there are only three companies that can produce HBM.
Sk Hynix – They are the market leader. Largest HBM supplier. They are the volume leader of HBM3E and HBM4
Samsung Electronics – They are a major supplier of HBM3E and HBM4. They will have a larger share of HBM4
Micron – They are the fastest growing supplier. They produce HBM3E and in volume production of HBM4 for Nvidia Rubin platform
HBM suppliers use stacks as a measurement for a GPU. NVIDIA B200 has 8 HBM3E stacks. Each stack holds 24 GB. Total memory is 8X24 GB for 192 GB. The thing to focus on is how many stacks each GPU will use.
Nvidia chips
H100 – 5 stacks
H200 – 6 stacks
B200 – 8 stacks
GB 300 – 8 stacks
Rubin - 8 stacks
Rubin Ultra – 16 stacks
Feynman is the next generation and based on progression it will probably be 16 stacks or more
AMD chips
MI300A – 8 stacks
MI300X – 8 stacks
MI325X - 8 stacks
MI350X – 8 stacks
MI400 is next generation and is 12 stacks
Intel Chips
Gaudi 2 – 6 stacks
Gaudi 3 – 8 stacks
Falcon Shores is next, but no announcement on stacks
Amazon has Tranium, Tranium2,Tranium3, and Inferentia2 that use HBM, but they don’t disclose amount of stacks
Google chips
TPU v4 – 4 HBM stacks
TPU v5e not disclosed
TPU v5p not disclosed
Ironwood TPU – 6 stacks
Microsoft is working on Maia 100, but has not disclosed how much HBM it will use
Meta is currently working on MTIA 300/400/450/500 that wil use HBM. They could use 4-8 HBM stacks but that is all speculation
Other companies of note that could be using HBM or invest in infrastructure are Apple, Broadcom, OpenAi, Tesla, xAI, Marvell, Alibaba,ByteDance, Baidu, Huawei, and IBM.
From the examples above it shows that HBM is being used in greater amounts per generation of GPU
Future uses for HBM besides data centers are humanoid robots, autonomous vehicles and industrial robots. The majority of industrial robots won’t be using HBM. We probably won’t see this until 2027 at the earliest.
The global HBM stack demand forecast:
2026 ~20-30 million stacks
2027 ~35-50 million stacks
2028 ~55-75 million stacks
2029 ~80-100 million stacks
2030 ~100-150 million stacks
If chipmakers can produce more energy efficient chips in the future that also means they will replace older generations creating demand later.
From an investor perspective I am very bullish. Every hyperscaler earnings call has basically said their business is growing and they are capacity constrained.
Google Cloud had a revenue growth rate of 82% YOY
Azure was 43%
AWS was 37%
I don’t see any reason not to stay invested at this time. Ai Infrastructure spending is turning into cloud revenue growth. It is validating the spending which supports continued HBM demand. Based on the earnings calls Microsoft, Amazon, and Google are making the best return on investment. Meta is the only hyperscaler that really isn’t showing the best return on investment. They could slow their Capex spending, but the other hyperscalers could step in for that demand if they continue to boost their revenue.
HBM is High Bandwidth Memory and what is being used in the AI chips. HBM sits next to the GPU. Currently there are only three companies that can produce HBM.
Sk Hynix – They are the market leader. Largest HBM supplier. They are the volume leader of HBM3E and HBM4
Samsung Electronics – They are a major supplier of HBM3E and HBM4. They will have a larger share of HBM4
Micron – They are the fastest growing supplier. They produce HBM3E and in volume production of HBM4 for Nvidia Rubin platform
HBM suppliers use stacks as a measurement for a GPU. NVIDIA B200 has 8 HBM3E stacks. Each stack holds 24 GB. Total memory is 8X24 GB for 192 GB. The thing to focus on is how many stacks each GPU will use.
Nvidia chips
H100 – 5 stacks
H200 – 6 stacks
B200 – 8 stacks
GB 300 – 8 stacks
Rubin - 8 stacks
Rubin Ultra – 16 stacks
Feynman is the next generation and based on progression it will probably be 16 stacks or more
AMD chips
MI300A – 8 stacks
MI300X – 8 stacks
MI325X - 8 stacks
MI350X – 8 stacks
MI400 is next generation and is 12 stacks
Intel Chips
Gaudi 2 – 6 stacks
Gaudi 3 – 8 stacks
Falcon Shores is next, but no announcement on stacks
Amazon has Tranium, Tranium2,Tranium3, and Inferentia2 that use HBM, but they don’t disclose amount of stacks
Google chips
TPU v4 – 4 HBM stacks
TPU v5e not disclosed
TPU v5p not disclosed
Ironwood TPU – 6 stacks
Microsoft is working on Maia 100, but has not disclosed how much HBM it will use
Meta is currently working on MTIA 300/400/450/500 that wil use HBM. They could use 4-8 HBM stacks but that is all speculation
Other companies of note that could be using HBM or invest in infrastructure are Apple, Broadcom, OpenAi, Tesla, xAI, Marvell, Alibaba,ByteDance, Baidu, Huawei, and IBM.
From the examples above it shows that HBM is being used in greater amounts per generation of GPU
Future uses for HBM besides data centers are humanoid robots, autonomous vehicles and industrial robots. The majority of industrial robots won’t be using HBM. We probably won’t see this until 2027 at the earliest.
The global HBM stack demand forecast:
2026 ~20-30 million stacks
2027 ~35-50 million stacks
2028 ~55-75 million stacks
2029 ~80-100 million stacks
2030 ~100-150 million stacks
If chipmakers can produce more energy efficient chips in the future that also means they will replace older generations creating demand later.
From an investor perspective I am very bullish. Every hyperscaler earnings call has basically said their business is growing and they are capacity constrained.
Google Cloud had a revenue growth rate of 82% YOY
Azure was 43%
AWS was 37%
I don’t see any reason not to stay invested at this time. Ai Infrastructure spending is turning into cloud revenue growth. It is validating the spending which supports continued HBM demand. Based on the earnings calls Microsoft, Amazon, and Google are making the best return on investment. Meta is the only hyperscaler that really isn’t showing the best return on investment. They could slow their Capex spending, but the other hyperscalers could step in for that demand if they continue to boost their revenue.
So much money flying around AI and memory. Hard for Wallstreet to keep up the cyclical nonsense.
More bullish news
Did the Motown lunch cruise and all around it was great. The buffet had a good food. Entertainment was great. Enjoyed the views on the river. Also the ship is really cool. Definitely recommend it.