My professional independent observer opinion (not based on my 2 years of working at Groq) would have me assume that their COGS to achieve these performance numbers would exceed several million dollars, so depreciating that over expected usage at the theoretical prices they have posted seems impractical, so from an actual performance per dollar standpoint they don’t seem viable, but do have a very cool demo of an insane level of performance if you throw cost concerns out the window.

[0]: https://www.nextplatform.com/2023/11/27/groq-says-it-can-dep...

Anyone with a serious interest in the total cost of ownership of Groq's system is welcome to email [email protected].

A guarantee to match the cheapest per token prices is sure a great way to lose a race to the bottom, but I do wish Groq (and everyone else trying to compete against NVIDIA) the greatest luck and success. I really do think that the great single batch/user performance by Groq is a great demo, but is not the best solution for a wide variety of applications, but I hope it can find its niche.

John doe and his friends will never have a need to have their fart jokes generated at this speed, and are more interested in low costs.

But we’d recently been doing call center operations and being able to quickly figure out what someone said was a major issue. You kind of don’t want your system to wait for a second before responding each time. I can imagine it making sense if it reduces the latency to 10ms there as well. Though you might still run up against the ‘good enough’ factor.

I guess few people want to spend millions to go from 1000ms to 10ms, but when they do they really want it.

It was also on my list of things to consider modifying for an AI accelerator. :)

There should be a podcast release (https://microarch.club/) in the near future that covers REX's history and a lot of lessons learned.

Come to think of it, this is one of the few places where asynchronous logic might be more than academic... Async logic is hard with complex control flows, which deep learning inference does not have.

(From a practical perspective, I know you were comparing to independently-clocked logic, rather than async logic)

I wonder whether async logic would be feasible for reconfigurable "Spatial Processor" type architectures [1]. As far as LPU architectures go, they fall in the "Matrix of Processing Engines"[1] family of architectures, which I would naively guess is not the best suited to leverage async logic.

1: I'm using the "Spatial Processor" (7:14) and "Matrix of Processing Engines" (8:57) terms as defined in https://www.youtube.com/watch?v=LUPWZ-LC0XE. Sorry for a video link, I just can't think of another single reference that explains the two approaches.

To answer your questions:

- Spatial processors are an insanely good fit for async logic

- Matrix of processing engines are a moderately good fit -- definitely could be done, but I have no clue if it'd be a good idea.

In SP, especially in an ASIC, each computation can start as soon as the previous one finishes. If you have a 4-bit layer, and 8-bit layer, and a 32-bit layer, those will take different amounts of time to run. Individual computations can take different amounts of time too (e.g. an ADD with a lot of carries versus one with just a few). In an SP, a compute will take as much time as it needs, and no more.

Footnote: Personally, I think there are a lot of good ideas in 80's era and earlier processors for the design of individual compute units which have been forgotten. The basic move in architectures up through 2005 was optimizing serial computation speed at the cost of power and die size (Netburst went up to 3.8GHz two decades ago). With much simpler old-school compute units, we can have *many* more of them than a modern multiply unit. Critically, they could be positioned closer to the data, so there would be less data moving around. Especially the early pipelined / scalar / RISC cores seem very relevant. As a point of reference, a 4090 has 16k CUDA cores running at just north of 2GHz. It has the same number of transistors as 32,000 SA-110 processors (running at 200MHz on a 350 nanometer process in 1994).

TL;DR: I'm getting old and either nostalgic or grumpy. Dunno which.

    Xeon Phi CPUs support (a.k.a. Knight Landing and Knight Mill) are marked as deprecated. GCC will emit a warning when using the -mavx5124fmaps, -mavx5124vnniw, -mavx512er, -mavx512pf, -mprefetchwt1, -march=knl, -march=knm, -mtune=knl or -mtune=knm compiler switches. Support will be removed in GCC 15.

you might be surprised but we're getting to the point that communicating over a super computer is on the same order of magnitude as talking across a numa node.

For machine learning, that's a good tradeoff.

Indeed, with some of the simpler architectures, I think computation could be moved into the memory itself, as long dreamed of.

(Simply sticking 32,000 SA-110 processors on a die would be very, very limited by interconnect; there's a good reason for the types of architectures we're seeing not being that)

They seem annoying. "The IPU has a unique memory architecture consisting of large amounts of In-Processor-Memory™ within the IPU made up of SRAM (organised as a set of smaller independent distributed memory units) and a set of attached DRAM chips which can transfer to the In-Processor-Memory via explicit copies within the software. The memory contained in the external DRAM chips is referred to as Streaming Memory™."

There's a ™ every few words. Those seem like pretty generic terms. That's their technical documentation.

The architecture is reminiscent of some ideas from circa-2000 which didn't pan out. It reminds me of Tilera (the guy who ran it was the Donald Trump of computer architectures; company was acquihired by EZchip for a fraction of the investment which was put into it, which went to Mellanox, and then to NVidia).

- Early mainframes / room-sized computers (era of vacuum tubes and discrete transistors), especially at the upper-end , where there was enough budget to have modern pipelined and scalar architectures.

- Cray X-MP and successors

- DEC Alpha / StrongARM (referenced SA-110)

Bad places to look are all the microcode architectures. These optimized transistor count, often sacrificing massive amounts of performance in order to save on cost. Ditto for some of the minicomputers, where the goal was to make an "affordable" computer. Something like the PDP was super-clever in cost-cutting, which made sense at the time, does much less to maintain performance.

There's a ton of long-forgotten cleverness.

So these specialized approach never stood a chance next to CPUS. Nowadays the ground is.. more fertile.

The problem was

1) If you took 3 years longer to build a SIMD architecture than Intel to make a CPU, Intel would be 4x faster by the time you shipped.

2) If, as a customer, I was to code to your architecture, and it took me 3 more years to do that, by that point, Intel would be 16x faster

And any edge would be lost. The world was really fast-paced. Groq was founded in 2016. It's 2024. If it was still hayday of Moore's Law, you'd be competing with CPUs running 40x as fast as today's.

I'm not sure you'd be so competitive against a 160GHz processor, and I'm not sure I'd be interested knowing a 300+GHz was just around the corner.

Good ideas -- lots of them -- lived in academia, where people could prototype neat architectures on ancient processes, and benchmark themselves to CPUs of yesteryear from those processes.

https://wow.groq.com/wp-content/uploads/2023/05/GroqISCAPape...

You also say that H200's work reasonably well, and that's reasonable (but debatable) for synchronous, human interaction use cases. Show me a 30b+ parameter model doing RAG as part of a conversation with voice responses in less than a second, running on Nvidia.

Or I got this completely wrong, and your solution enables use-cases that are simply unattainable on mainstream (Nvidia/AMD) hardware, making TCO argument less relevant?

You may have meant that nobody has a stack that uses clustering or DSM with low-latency interconnects. If so, then that might be worth developing given prior results in other low-latency domains.

DRAM needs to be refreshed every X cycles.

This means you don't know the time it takes to read from memory. You could be reading at a refresh cycle. This circuitry also adds latency.

It would great if a company or others with AI hardware were willing to do production runs of chips sold at cost specifically to make open, permissive-licensed models. As in, since you’d lose profit, the cluster owner and users would be legally required to only make permissive models. Maybe at least one in each category (eg text, visual).

Do you think your company or any other hardware supplier would do that? Or someone sell 2500 GPU’s at cost for open models?

(Note to anyone involved in CHIPS Act: please fund a cluster or accelerator specifically for this.)

reformed HPC person here.

Yes, but not latency optimised in the case here. HPC is normally designed for throughput. Accessing memory from outside your $locality is normally horrifically expensive, so only done when you can't avoid it.

For most serving cases, you'd be much happier having a bunch of servers with a number of groqs in them, than managing a massive HPC cluster and trying to keep it both up and secure. The connection access model is much more traditional.

Shared memory clusters are not really compatible with secure enduser access. It is possible to partition memory access, but its something thats not off the shelf (well that might have changed recently.) Also, shared memory means shared fuckups.

I do get what you're hinting at, but if you want to serve low latency, high compute "messages" then discrete "APU" cards are a really good way to do it simply (assuming you can afford it). HPCs are fun, but its not fun trying to keep them up with public traffic on them

I agree it’s harder to manage with less, fine-grained security. People were posting Groq chips at $20k each, though. With that, we’re talking whether the management of it is worth it for installations costing six or more digits. That might be more justifiable if an alternative saves them a good chunk of six or more digits.

Their main advantage is a solution that’s ready to go :)

I built one, should be live soon ;-)

I believe that this is doable - my pipeline is generally closer to 400ms without RAG and with Mixtral, with a lot of non-ML hacks to get there. It would also definitely be doable with a joint speech-language model that removes the transcription step.

For these use cases, time to first byte is the most important metric, not total throughput.

The most interesting applications of LLMs are not chatbots.

What are they then? Every use case I’ve seen is either a chatbot or like a copy editor which is just a long form chatbot.

Think about the implications of that. I bet you can come up with some pretty cool use cases that don't involve you talking to something over chat.

One example:

I think we'll be seeing a lot of "general detectors" soon. Without training or predefined categories, get pinged when (whatever you specify) happens. Whether it's a security camera, web search, event data, etc

In your opinion, what are the most interesting?

You only need a few tokens, not the full 500 tokens response to run TTS. And you can pre-generate responses online, as ASR is still in progress. With a bit of clever engineering the response starts with virtually no delay, the moment its natural to start the response.

https://www.youtube.com/watch?v=pRUddK6sxDg&t=235s

Once it flawlessly understands when it is being spoken to/if it should speak based on the topic at hand (like we do) then it'll be amazing.

I wonder if ML models can feel that feeling of wanting to say something so bad but having to wait for someone else to stop talking first ha ha.

Is your version of that on a different page from this chat bot?

I'm not so convinced they have a Tok/sec/$ advantage at all, though, and especially at medium to large batch sizes which would be the groups who can afford to buy so much silicon.

I assume given the architecture that Groq actually doesn't get any faster for batch sizes >1, and Nvidia cards do get meaningfully higher throughput as batch size gets into the 100's.

This is because the relationship between Tok/s/u, Tok/s/system, Batching, and Pipelining is a complex one that involves compute utilization, network utilization, and (in particular) a host of compilation techniques that we wouldn't want to share publicly. Maybe we'll get to that level of transparency at some point, though!

As far as Batching goes, you should consider that with synchronous systems, if all the stars align, Batch=1 is all you need. Of course, the devil is in the details, and sometimes small batch numbers still give you benefits. But Batch 100's generally gives no advantages. In fact, the entire point of developing deterministic hardware and synchronous systems is to avoid batching in the first place.

    I assume given the architecture that Groq actually doesn't get any faster for batch sizes >1

We're just that much better at squeezing tokens out of transistors and optic cables than GPUs are - and you can imagine the implications on Watt/Token.

Anyways.. wait until you see our 4nm. :)

"just to serve a single model" could be easily fixed by adding a single LPDDR4 channel per LPU. Then you can reload the model sixty times per second and serve 60 different models per second.

I would expect the model loading to take basically zero percent of the time in the above workflow

IDGAF about any of that, lol. I just want an API endpoint.

480 tokens/sec at $0.27 per million tokens? Sign me in, I don't care about their hardware, at all.

That being said, until there's another option at anywhere that speed.. That point is moot, isn't it :)

For now, Groq is the only option that can let you build an UX with near-instant response times. Or a live agents that help with a human-to-human interaction. I could go on and on about the product categories this opens.

I think the problem is that for realistic TTS you need quite a few tokens because the prosody can be affected by tokens that come a fair bit further down the sentence, consider the difference in pitch between:

"The war will be long and bloody"

vs

"The war will be long and bloody?"

So to begin TTS you need quite a lot of tokens, which in turn means you have to digest the prompt and run a whole bunch of forward passes before you can start rendering. And of course you have to keep up with the speed of regular speech, which OpenAI sometimes struggles with.

That said, the gap isn't huge. Many apps won't need it. Some use cases where low latency might matter:

- Phone support.

- Trading. Think digesting a press release into an action a few seconds faster than your competitors.

- Agents that listen in to conversations and "butt in" when they have something useful to say.

- RPGs where you can talk to NPCs in realtime.

- Real-time analysis of whatever's on screen on your computing device.

- Auto-completion.

- Using AI as a general command prompt. Think AI bash.

Undoubtably there will be a lot more though. When you give people performance, they find ways to use it.

I can imagine a way might be found to host a base model and a bunch of LoRA's whilst using barely more ram than the base model alone.

The fine-tuning could perhaps be done in such a way that only perhaps 0.1% of the weights are changed, and for every computation the difference is computed not over the weights, but of the output layer activations.

Disclaimer: I'm one of the authors.