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Jan 31 32 tweets 8 min read Read on X
This key chart from @SemiAnalysis_ appears to have been the key source for claims of "50,000 Hoppers" and more detailed disclosure on their CapEx buildup analysis ("$1.3B").

But the table has errors/inconsistencies. More significantly, key assumptions don't pass sanity checks. https://semianalysis.com/2025/01/31/deepseek-debates/
1⃣ First thing you might notice is that it says 60,000 in the Total column.

But the A100s aren't "Hoppers". So the 50,000 is just the last three columns.

Ok, so far so good. Image
This is where it starts to get confusing.

The Total column only seems to SUM the first three columns.

Also "ASP" and "per GPU" are average numbers, so you cannot just sum it up. You need to do a weighted average. So the Total figures make no sense. Image
While the top "# of GPUs" line sums up all four columns, down below only the first three columns are added together.

So the $1.3B capex figure doesn't include the H100s?

Careless formula error? Or do we read more into it? Image
And then this last line seems to be a sum of Server CapEx and "Cost to Operation".

But TCO (4y Ownership) implies that it should be a per year figure (the label says "$m/hr").

I think it should be the sum of the Server CapEx + Cost to operation divided by 4 but hard to say.Image
Anyway I put together what I think is a corrected version of this chart if we are counting all 60,000 chips it is $1.6B and $640M p.a. of TCO. Image
If we are only doing "Hoppers" then it is $1.4B of CapEx and $545M p.a. of TCO. Image
2⃣ Ok all these might just be chalked up to basic first-year analyst spreadsheet errors and may or may not impact the ultimate analysis.

More substantively ... how credible is the "50,000 Hoppers" estimate in the first place?
The article links to a proprietary "Accelerator Model" that is paywalled so difficult for me to confirm rationale here beyond pure speculation ...

... but what I can do is run a sanity check based on basic understanding of the economics of fund management.
Some people out there are saying High Flyer managed $8B in funds, implictly assuming that those sums could support such a large CapEx number.

But that's not how the hedge fund business works.
What we know about High Flyer and DeepSeek:

▪️ High-Flyer was a quant fund with $8 billion in AUM.
▪️ DeepSeek was "self funded" by High-Flyer.

Can a $7B AUM hedge fund self fund $1.6B of capex? Highly unlikely. Image
The economics of a hedge fund are typically a management fee and performance fees.

1%/20% is typical.

So on $7B, High Flyer would generate an estimated $70M in management fees.
Performance fees are calculated only on gains. Note below High Flyer's performance of ~13% annualized since 2017.

However, note also how returns have basically been down since 2021. There are unlikely to have been significant performance fees since 2021. https://www.ft.com/content/357f3c68-b866-4c2e-b678-0d075051a260
So while strong fund performance through '21 — albeit likely on much lower AUM as it was ramping up — could have arguably funded the reported purchase of ~¥1B in GPUs in 2021, it is unlikely that the hedge fund itself could have continued self-funded that level of CapEx going forward.Image
$130M is already an extreme amount of CapEx for an $7B fund.

Blackstone, which has more than 100x the AUM (which drives revenue), has an annual capex budget of ~$250M.

Goldman Sachs generates close to 1,000x the revenue as High Flyer, and has an annual CapEx budget of ~$2.5B.
Similarly companies like Alibaba, Baidu and Bytedance generate tens of billions in revenue, orders of magnitude above High Flyer.

They can afford to spend billions buying nVidia chips and building out their own internal datacenters.
There is absolutely no way an $8 billion fund (with flat/negative returns over the period) could have "self-funded" another $1.6 billion in CapEx.

You know what it could have reasonably funded? 2,048 H800 datacenter worth ~$70M ...
... and even here that is quite an extreme CapEx ratio for a fund generating a total of ~$70M (maybe) of management fees that need to pay for fund operations themselves.
So the only possible way that High Flyer could have funded the purchase of another even just 10,000 H800s would have been to have raised secret outside funding for DeepSeek, which of course contradicts the article itself.
Of course, there are now rumors of that swirling around — maybe as people figure out the above math — but then we should just be up front that these estimates are based on pure unsubstantiated speculation and just leave it at that.
A model (even one riddled with basic formula errors) is only as good as its assumptions and it looks like the assumptions here of DeepSeek having access to "50,000 Hoppers" to build out v3 are built on an increasingly shaky foundation.
This is what it looks like with DeepSeek's actual reported cluster of 2,048 H800 GPUs.

These still seem high, but are at least within the realm of reason. Image
High-Flyer Quant Fund CEO Lu Zhenghe disclosed in an interview in 2020 that "70% of annual revenue is reinvested back into research and development" with strong implication that it is mostly production related, and not CapEx.

pekingnology.com/p/ceo-of-deeps…Image
P.S. A very common Excel mistake is when you add a column and formula doesn't pick it up.

I suspect this is what happened here: Analyst added "H100" column, Total formula didn't pick it up + while top row is easy to spotcheck, bottom ones were missed

P.P.S. Image
P.P.P.S. Call me when AGI can figure out Excel, amirite @abcampbell ?
P.P.P.P.S. This is just a very quick estimate of the lifetime revenue that High Flyer funds would have generated with accompanying assumptions.

~$400 million available for reinvestment into both R&D and CapEx.Image
As mentioned earlier, this estimated P&L would support the self-funded buildout of the initial dataclusters (up to 10,000 A100s) through 2021 but hard to see how it could have self-funded anything close to the implied OoM increase in CapEx.

The 10,000 A100s bet was already an extraordinary bet for Liang / High Flyer, with parallels to Elon Musk investing nearly all his PayPal sale proceeds into Tesla + SpaceX.

It's also inconsistent with Quant Fund CEO's comments in 2020 of redirecting reinvestment efforts at R&D (a.k.a. smart people) instead of CapEx.
And yes it makes much more sense that DeepSeek rented from the bigger players and didn’t even own the “2,048 H800s” that they mentioned in the v3 paper.

The H800s that they owned would have been for limited R&D purposes, like trying to hack the PTX code.

So bottom line is I think we violently agree the 50,000 number makes no sense.
@YouJiacheng @angelusm0rt1s @blob_watcher Until they close that loophole
@FarazKh78685502 @dylan522p @SemiAnalysis_ Just to save you the suspense - no Liang doesn’t have a trust fund

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More from @GlennLuk

Feb 14
There's of course a certain level of irony with rumors of 50/50 JV with TSMC being considered with "tech transfer" when juxtaposed next to complaints about China's mandated use of 50/50 JVs and tech transfer, e.g. in the automobile sector in the 90s and 2000s.
Instead of correctly viewing it dispassionately as a rather effective structure to transfer tacit knowledge in a "learn by doing" activity like manufacturing, the 50/50 JV structure itself was demonized and associated with allegations of IP theft and unfair trade practices.
This negative association with China is perhaps a key reason why it has taken so long for the structure to be seriously considered in the U.S. despite making a ton of sense.

But now that we seem to be crossing this psychological barrier, it is important to keep in mind some of the actual lessons learned from China's rather effective use of 50/50 joint ventures and technology transfer.
Read 16 tweets
Feb 7
Rising U.S. goods trade deficit is (in part) a mirror of the rising power and profitability of U.S. MNCs that generate large profits offshore that are accumulated in tax havens.

You can see this by walking through the balance of payments.
Everyone knows that the U.S. goods deficit has been growing.

The CSIS (Pettis/Setser) explanation is that this reflects imbalances in the global economy, e.g. China “overcapacity”.

The problem as I have been saying is that the goods trade is only a subset of trade.
When a U.S. MNC like Apple sells an iPhone in China, most of the value is in intangible technology and brand.

Chinese households send money to Apple. The trade happens in China because the phone is manufactured there.

So most of the purchase price does not hit the goods trade. Image
Image
Read 20 tweets
Jan 29
One focus area for DeepSeek was going "lower level" with PTX.

Here's a nice explainer on CUDA/PTX 👇.

While this is an admittedly arcane/technical subject, it actually provides critical insights into:

1⃣ the development cost question
2⃣ export controls
3⃣ future trajectories
First some quick background: PTX is a low-level coding language analogous to Assembly.

This is "deep" software engineering: you are now talking in the native language of the machines, with the thinnest of abstraction layers.

I discussed more here, too:

To help visualize:

The next level down from PTX/Assembly would be to view it all as a string of numbers e.g. the Matrix.

In modern semiconductor-based computing, it'd be a string of 0s and 1s, a.k.a. "binary" to accord with the physical on/off nature of electricity and circuits.
Read 22 tweets
Jan 28
If people freaked out about DeepSeek wait until they hear about how it is scaling inference.
“Third of the cost” is

1) Likely conservative, and

2) Didn’t require EUV
DeepSeek will increase demand for chips because of Jevon’s Paradox, yes.

But whose chips?
Read 32 tweets
Jan 25
Three reasons why this is the wrong take:

1⃣ Continent-sized economies follow different economic laws (specialization vs. self-sufficiency)

2⃣ Optimal sector allocation

3⃣ China's STEM workforce is young
1⃣ Continent-sized economies follow different economic laws (specialization vs. self-sufficiency)

Larger economies can be more self-sufficient (pursue higher levels of autarky) without losing the benefits of specialization.

2⃣ Optimal sector allocation

There is fixed "societal alpha" in certain sectors like law and finance.

Over-allocation of top talent to these sectors has earlier diminishing marginal returns** than others like engineering (where you build things and/or can export)

** top lawyers arguing against each other cancel each other out; top finance folks fight for the same pool of fixed alpha
Read 9 tweets
Jan 25
This is a key point about EVs. Many think it is about the electrification.

It is really more about the tech (the "I"): software-defined UX + tech component integration.

Keep in mind this comes from CATL's Co-Chairman, whose batteries are tied to the electrification aspect.
A prime example of this is how PHEVs/EREVs are the fastest-growing segment in China right now.

They are built on the same modern platforms as BEVs, where the ICE is simply another integrated component to extend range beyond battery-only.

These "EIV" cars (a.k.a. "New Energy Vehicles") are the 2020s equivalent of iPhones going up against "feature phones" (and extensions of them, like Blackberries) from the smartphone transition era during the 2000s.

Read 14 tweets

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