Two weeks into an accelerator evaluation, the spreadsheet stops mattering.
A four person research team scores two cards on memory, bandwidth and power draw.
The second one wins in every column.
Then someone ports the training code and the build dies on the first custom kernel.
ByteDance, Alibaba and Tencent ran that math at a much bigger scale this week.
According to a Reuters report, they placed orders for more than 3 million NVIDIA RTX PRO 5500 units, about $35 billion together, and NVIDIA has told partners it can fill only 5 to 10 percent of that.
The B300 is not authorized for China and the H200 carries a licensing condition, so Huawei's Ascend should be the obvious pick.
On paper it even looks better, with 128GB of HiBL 1.0 memory and 1.4 to 1.6 TB/s of bandwidth against 84GB of GDDR7 ECC and 1.398 TB/s on the RTX PRO 5500.
Huawei has to stack multiple chips to make up for the raw performance gap that CUDA covers in software.
Three of China's biggest technology firms accepted a 5 percent chance of delivery to stay on the stack their code already runs.
Here is what that means below their scale.
If your work fits on a single accelerator, you do not need a place in that queue at all.
An RTX 3090 at $0.15/hr and storage at $2.49/TB gets a model trained or a pipeline tested this week instead of next quarter.
Buy Now Pay Later means zero upfront and payment after one month.
When your team last tried a second accelerator vendor, how long did the port take before the first successful training run?
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Originally posted on linkedin_personal