A hundred ways to die for AI chips

A few days ago, Nvidia made history by becoming the first chip company with a market value of $2 trillion. At the same time, a start-up called Groq was born, threatening to "catch up with Nvidia within three years".

The confidence of Groq's cruel words comes from the chip it developed - LPU.

The full name of LPU is Language Processing Unit, which translates to Language Processing Unit, which is a new type of AI accelerator. With its support, the reasoning speed of large models is surprisingly fast, and it can output long answers in an instant, while GPT-4, which uses NVIDIA chips, can only jump out word by word.

THE RESPONSE SPEED OF THE FORMER CAN REACH UP TO 500 T/S, WHILE THE LATTER IS ONLY 40 T/S[1].

What is this concept? It's the equivalent of generating 300 words in one second and a Hamlet-length essay in seven minutes.

The team behind Groq is also a star-studded group, with CEO Jonathan Ross being one of the designers of Google's original TPU. Eight of the 10 members of Google's early TPU team switched to Groq.

Since its inception in 2016, Groq has attracted a lot of attention from the market. In 2020, Groq's chip was adopted by Argonne Laboratories in the United States. In 2021, Groq received investment from Tiger Global and other institutions, with a valuation of more than $1 billion.

However, Nvidia did not pay attention to Groq's various "provocations" at all. In contrast, after the news of "Ultraman's $7 trillion core building" came out, Huang Jenxun at least came out and said two words.

After all, Lao Huang is very familiar with Groq's routines at the moment.

文字游戏

At present, the main dilemma restricting the development of AI chips is the memory wall: the transmission speed of memory is much slower than the computing power of the processor, resulting in a delay in performance.

If you think of the chip as a restaurant, then the memory is the warehouse and the processor is the kitchen.

Warehouse delivery is slower than kitchen cooking, severely limiting the restaurant's ability to deliver food. Therefore, chip manufacturers, including NVIDIA, are making a fuss about "warehouse (memory)". And the secret of Groq's crushing Nvidia is also hidden in the memory structure.

There are actually two types of memory used by traditional computers:

DRAM has a larger capacity but slower transfer speeds and acts as the main memory, while SRAM has a smaller capacity but extremely fast transfer speeds and acts as a secondary cache. The two have always been a cooperative relationship, and one is indispensable.

However, in pursuit of extreme speed, Groq abandoned DRAM and let SRAM carry the banner of LPU main memory.

It is equivalent to cutting down the large warehouse far away from the kitchen and piling all the ingredients directly in the vegetable basket next to the kitchen. Under this design idea, the LPU not only reduces the speed of the game, but also easily bypasses the two most expensive technologies: HBM and advanced packaging.

This also forms the core of Groq's other big hype: cheap.

According to Semi Analysis' teardown, the material cost of the LPU is only $1,050 due to its architectural advantages. In comparison, the global frenzy of H100 chips last year had a material cost of $3,700 [2].

At the selling price level, the price of an LPU is $20,000, much lower than the $35,000 price of the H100.

But history tells us countless times that most corner overtaking ends with Arita going downhill, and LPU is no exception.

Large models also have high requirements for memory size. The larger the number of parameters, the more data needs to be stored at runtime.

Although SRAM is fast, the disadvantage is that it has a small capacity, usually only 4-16MB. After years of hard work, the Groq R&D team was able to increase the capacity of the LPU to 230MB. The memory capacity of an H100 is 80GB, which is about 356 times different. No matter how the vegetable basket is upgraded, it can't be compared with the warehouse after all.

The only way to fit all the ingredients is to pile up the baskets. As a result, the number of LPUs required to run a model with the same parameters is much higher than that of a GPU.

Jia Yangqing, former vice president of Ali Technology, calculated an account:

For example, to run the LLaMA 70b model, it takes 572 LPUs and costs $11.44 million, but if you change to the H100, you actually only need 8 LPUs, and the total price is about $300,000 - the so-called "cheap" is not valid at all.

As soon as there are more chips, the overall power consumption cost has also risen sharply. The LPU consumes at least $254,000 per year in electricity bills, while the H100 costs at most $24,000.

As it turns out, Groq's lead is just a play on words that hide the core message. The "high speed" it advertises comes at an almost exaggerated cost of use: three years of running the LLaMA 70b model will cost 32 times more to use than Nvidia's GPU.

Of course, Groq's R&D team clearly knows this. Choosing this time to make a big splash is more like a move to pull investment.

In fact, this is not the first time that Groq has publicly "gained popularity".

When the GPT Store was first released, Groq sent a letter to OpenAI's Ultraman, mocking the slowness of using GPTs as "reading War and Peace late at night." When Musk's chatbot Grok was released, it also ran to taunt his face, questioning Grok for plagiarizing his name.

Groq is just one of the many startups that have been touted as an "alternative to NVIDIA" over the past few years. At present, there has been a serious traffic jam in this corner of overtaking NVIDIA.

Lesson

The most intuitive reference sample for Groq comes from the UK-based company Graphcore.

At the beginning of Graphcore's birth, it was also a playbook with "ingenious technical routes, cool and stunning presentation videos, and performance data to kill peers", and the IPU of its flagship product is similar to Groq's LPU design idea, which also replaces DRAM as chip memory with high-speed SRAM to achieve the ultimate transmission performance.

At the same time, Graphcore adapted its chip architecture to focus on high-performance computing.

It has self-consciously stated that it "does not compete directly with NVIDIA" and only targets special fields such as chemical materials and medical treatment, which require a large amount of high-performance computing.

Two founders of Graphcore

In 2019, Microsoft became a major customer of Graphcore's first IPU. In May 2020, Microsoft scientists built the IPU into Microsoft's Azure operating system for identifying chest X-rays. The scientist said:

"The Graphcore chip can do the job in 30 minutes, compared to the GPU in 5 hours. ”

Between 2016 and 2020, Graphcore raised US$710 million in funding at a valuation of US$2.77 billion, and was once seen as the hope of the entire village in the UK and Europe. Graphcore's investors include not only tech giants such as Microsoft, Samsung and Dell, but also top venture capitalists such as Sequoia Capital and Baiji Investments.

Compared to today's Groq, Graphcore can only be said to be as good as it is today. But Graphcore's subsequent development fell far short of what was expected at the time.

In 2022, Graphcore's full-year revenue was only $2.7 million, which was one-ten-thousandth of Nvidia's revenue for the same period, and it was down 46% from the previous year, bringing the loss to $200 million. In 2023, the wave of artificial intelligence will break out, and NVIDIA's performance will skyrocket again. When the H100 was hard to find, Graphcore thought it could at least pick up the Nvidia leak, but it didn't even drink the soup.

Last year, Graphcore's founder, Nigel Toon, wrote an "open letter" to the UK Prime Minister asking for a subsidy.

He writes that "Graphcore has set aside up to 3,000 IPU chips for the UK to serve the entire country", almost explicitly asking for money [4].

Not long ago, Graphcore, which was losing money and raising money, went to the last option: to make an acquisition. According to The Telegraph, it could sell for more than $500 million – less than one-fifth of what it was at its highest valuation in 2020 [5].

At that time, the challengers led by Graphcore were menacing and menacing, and there was a sense of déjà vu that the eight major factions were besieging Guangmingding. However, now most of them have scattered into the form of birds and beasts.

In March last year, the chip company Mythic was on the verge of bankruptcy due to running out of funds, and when it was about to drown, it finally waited for the reins of life and got $13 million in financing.

Another chip company, Blaize, also had a similar dilemma, and finally survived with the investment of Middle Eastern tycoons.

Of the remaining companies, Habana is probably the only one alive and well — it was acquired by Intel for $2 billion, retaining the right to operate independently.

From Graphcore to Mythic, these chip companies have different technology paths, but the reasons for their failures are surprisingly the same. In fact, Groq, which is on fire today, is also very likely to fall in the same place:

Chips can't be sold.

A real moat

Nvidia's GPU is certainly powerful, but its routine of selling chips is the real moat.

Every year, NVIDIA invests a considerable part of its R&D funds to build systematic capabilities around GPUs. Of course, this is the statement of high emotional intelligence, and the term of low emotional intelligence is the development of products that are "bundled" together - this is the most solid wall of NVIDIA. Currently, NVIDIA's walls have 3 floors:

The first layer of the city wall is CUDA's programming ecosystem.

As we all know, the initial use cases of GPUs were for gaming and video image rendering. In the early days, some Wall Street elites occasionally used the parallel computing power of GPUs to run deals, but it didn't go viral because of the large amount of code that needed to be rewritten.

Huang believes that GPUs can be used in more areas, so in 2006 he introduced CUDA, a software architecture that lowers the barrier to entry for programming, and bundled it with his own GPUs.

Later, Apple and AMD both launched similar platforms, but by this time, the CUDA ecosystem had already been built, and it was steadily moving forward in a virtuous circle of "the more people use it, the better CUDA will be, and the more new developers tend to choose CUDA".

Today, CUDA can make it possible to significantly reduce the cost of using NVIDIA GPUs.

A private cloud CEO once said in an interview that AMD cards are theoretically not unusable, but it will take two more months to debug these cards into normal operation[6] - who to place the order, the answer is self-evident.

In 2017, Nvidia even gifted a V100 chip to AI researchers

The second layer of the city wall is the high-speed transmission of NV-Link.

In a data center, it is impossible to use only one AI chip. However, if two AI chips are connected together, the actual computing power will inevitably be less than 2, because the speed of data transmission is slower than the computing power of the chip, and there is still loss in the process.

Obviously, in a data center with a rapidly expanding number of GPUs, solving the data transfer problem is key.

In 2016, when NVIDIA provided GPUs for IBM servers, it used its self-developed NVLink technology for the first time, with a bandwidth of up to 80G/s, a 5-fold increase in communication speed, and a 14% increase in performance. In the following years, NVIDIA iterated NVLink technology while stipulating that the technology must be bound to its own chips.

Although the routine is simple and straightforward, it is effective.

The third layer of the city wall is NVIDIA's "alliance of hardcore good brothers".

In the past year, Nvidia has been one of the world's leading AI investors, even surpassing top investment institutions such as A16Z and Sequoia.

According to foreign media statistics, Nvidia made at least 35 AI investments last year, including: Inflection AI, founded by the former co-founder of DeepMind, Mistral, a European AI unicorn, Hugging Face, the world's largest open-source model community, and so on [7].

Inflection AI创始人苏莱曼

The purpose of active investment is actually very simple: When Huang knocks on the doors of these companies with dollars and H100 chips, no one will refuse Nvidia anymore.

All of this is enough for the vast majority of companies on the market to be tied to death on the ship of NVIDIA.

In the face of powerful NVIDIA GPUs, startups of all stripes may have a way to create products that match the performance. However. Nvidia's routine of selling chips has always made it difficult for these companies to resist. So the failure of challengers like Graphcore is really not a lack of effort.

As the current hype gradually fades into silence, Groq has to ask the same question: who will climb three mountains to buy LPU?

End

On February 23, Nvidia's market capitalization exceeded $2 trillion. It has only been 9 months since the last 1 trillion.

The explosion of Groq has caused the market to discuss the same question again: Is there anyone who can call Nvidia?

Unfortunately, Nvidia is far more powerful than people think. The wave of artificial intelligence has provided a big cake for the chip industry, and Nvidia does not mean to share it, but the whole end is stuffed into its pocket.

The CEO of chip startup Mythic once said angrily that artificial intelligence is on fire, but it is more difficult for them to raise funds, and it is Nvidia that "spoils the environment".

According to PitchBook data, as of the end of August 2023, U.S. chip startups raised $881.4 million, about half of the same period in 2022. The change in the number of transactions is even more striking: from 23 to 4 [8].

Graphcore and Mythic are all familiar with the past, so it's no wonder that Groq is playing with words. Faced with such a behemoth that "doesn't give soup to drink", what can Groq do?

Resources

[1] Jay Scambler，X

[2] Groq Inference Tokenomics: Speed, But At What Cost，Semi Analysis

[3] The fastest inference chip for large models changed hands overnight: built by Google's TPU entrepreneurial team, qubits

[4] OPEN LETTER TO THE UK PRIME MINISTER ON INVESTMENT IN EXASCALE COMPUTE，Graphcore

[5] British AI champion explores foreign sale，The Telegraph

[6] Nvidia H100 GPUs: Supply and Demand，GPU Utils

[7] Nvidia emerges as leading investor in AI companies，FT

[8] Nvidia's dominance in AI chips deters funding for startups，reuters

封面图片来自 shotdeck

Editor: Chen Bin

Visual Design: Shu Rui

Editor in charge: Chen Bin

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