3 ms·

The claim that the next generation would be 35x faster, felt like an "Osborne moment" to me, but if demand is robust enough...

wmf 2y ago
Nvidia is doing the same thing. They announced B100 before H200 shipped and a few hours ago they started talking about R100 before B100 shipped.
ipsum2 2y ago
(Re: Osborne effect) It's going to be released in 2 years. Rarely can businesses wait that long, they're going to be ordering the MI300 now.
- latchkey 2y ago
  [deleted] See below, I did not understand the Osborne effect comment.
  - ipsum2 2y ago
    You're going to wait for the MI350 and not order any more MI300s?
    - latchkey 2y ago
      Weird that I got downvoted on the above. I'm buying and deploying MI300x's today and will buy whatever AMD comes out with next.
      - ipsum2 2y ago
        You were probably downvoted because you were shilling your company, and you misunderstood the comment. "The Osborne effect is a social phenomenon of customers canceling or deferring orders for the current, soon-to-be-obsolete product as an unexpected drawback of a company's announcing a future product prematurely. It is an example of cannibalization."
        latchkey 2y ago
        Shilling is ok on a topic directly related to my business. You're right on the Osborne effect though! Thanks for that. We are definitely not doing that. To clarify: When we started, MI300x was not officially announced yet, so we were planning on buying MI250's. Due to everything taking longer than expected around starting the business and receiving funding, by the time we had money in the bank, it was time to buy MI300x. Going forward, we are buying MI300x today and will continue to buy AMD MI series as they are released in the future.
        krasin 2y ago
        Since we're on the topic of your business: I am training a decent amount of neural nets nowadays (mostly, around the new-gen robotics policies) and use vast.ai instances with 8x RTX 4090 cards. I've been interested to give 8x MI300x a try, as they are supposed to be cheaper per FLOPs, but it looks like your service does not provide on-demand pay-per-second instances. Any plans to change that?
        latchkey 2y ago
        I would love nothing more than to be able to enable on-demand GPUs, but unfortunately this is a limitation from AMD right now. We can't do PCIe pass through to a virtual machine, it just doesn't work. This is why our minimum is 8 right now. If you look at all of our competitors, they have the same issue. Even Azure "VM", is 8 at a time, but they are all sold out due to high demand. It kind of makes sense since their history is only supporting the high end GPUs in their HPC solutions, where they don't use VM's. They've committed to us directly that they will fix this issue. I updated our pricing page to note this.
        trogdor 2y ago
        >I updated our pricing page to note this Nice job on your website! Sincerely, Someone who recently gave you a hard time for not having info on your website.
- karma_pharmer 2y ago
  Or they're trying to distract attention from the fact that they've already sold out 100% of the fab capacity available to produce these chips for the next two years. So really, they lose nothing. They've already booked sales of everything there is to sell. So might as well now turn attention to those who might be customers two years from now, and make them feel like the wait will be worth it.
Netcob 2y ago
In AI, that doesn't sound too surprising to me right now. I just experiment with some local LLMs, but the differences are pretty huge: Llama 3 8B, Raspberry Pi 5: 2-3 Tokens/second (but it works!) Llama 3 8B, RTX 4080: ~60 Tokens/second Llama 3 8B, groq.com LPU, ~1300 Tokens/second Llama 3 70B, AMD 7800X3D: 1-2 Tokens/second Llama 3 70B, groq.com LPU, ~330 Tokens/second There seem to be huge gaps between CPU, GPU and specialized inference ASICs. I'm guessing that right now there aren't many genius-level architecture breakthroughs, and that it's more about how much memory and silicon real estate you're willing to dedicate to AI inference.
- SushiHippie 2y ago
  What quantization levels did you use? I think groq doesn't use quantization, so the gap between your hardware and groq would be even further apart.
  - kkielhofner 2y ago
    > I think groq doesn't use quantization, so the gap between your hardware and groq would be even further apart. To my knowledge this isn't (absolutely) publicly known but users on /r/LocalLLaMA and elsewhere have provided some pretty clear examples that Groq is almost certainly quantized. Which makes sense considering their memory situation... An entire GroqRack (42U cabinet) has 14GB of RAM which means it likely can't even reasonably run llama3 8b in BF16/FP16. Let alone 70b, Mixtral, etc. The amount of hardware required to run their public-facing hosted product likely takes up an obscene amount of floor space, even in int4. Their docs for GrowFlow describe int8 quantization but their toolkit is heavily dependent on ONNX, which has had recent tremendous work in terms of different post training quantization strategies and precisions. However, the power efficiency vs performance is very good, potentially to the point of being able to use very cheap datacenter/co-location space that isn't capable of meeting the power and (air) cooling densities of datacenter AMD and Nvidia GPU products. Interestingly I have access to a GroqRack system that I'm hoping to be able to spend some time on this week.
    - SushiHippie 2y ago

wmf 2y ago

Nvidia is doing the same thing. They announced B100 before H200 shipped and a few hours ago they started talking about R100 before B100 shipped.

ipsum2 2y ago

(Re: Osborne effect) It's going to be released in 2 years. Rarely can businesses wait that long, they're going to be ordering the MI300 now.

latchkey 2y ago
[deleted] See below, I did not understand the Osborne effect comment.
- ipsum2 2y ago
  You're going to wait for the MI350 and not order any more MI300s?
  - latchkey 2y ago
    Weird that I got downvoted on the above. I'm buying and deploying MI300x's today and will buy whatever AMD comes out with next.
    - ipsum2 2y ago
      You were probably downvoted because you were shilling your company, and you misunderstood the comment. "The Osborne effect is a social phenomenon of customers canceling or deferring orders for the current, soon-to-be-obsolete product as an unexpected drawback of a company's announcing a future product prematurely. It is an example of cannibalization."
      - latchkey 2y ago
        Shilling is ok on a topic directly related to my business. You're right on the Osborne effect though! Thanks for that. We are definitely not doing that. To clarify: When we started, MI300x was not officially announced yet, so we were planning on buying MI250's. Due to everything taking longer than expected around starting the business and receiving funding, by the time we had money in the bank, it was time to buy MI300x. Going forward, we are buying MI300x today and will continue to buy AMD MI series as they are released in the future.
        krasin 2y ago
        Since we're on the topic of your business: I am training a decent amount of neural nets nowadays (mostly, around the new-gen robotics policies) and use vast.ai instances with 8x RTX 4090 cards. I've been interested to give 8x MI300x a try, as they are supposed to be cheaper per FLOPs, but it looks like your service does not provide on-demand pay-per-second instances. Any plans to change that?
        latchkey 2y ago
        I would love nothing more than to be able to enable on-demand GPUs, but unfortunately this is a limitation from AMD right now. We can't do PCIe pass through to a virtual machine, it just doesn't work. This is why our minimum is 8 right now. If you look at all of our competitors, they have the same issue. Even Azure "VM", is 8 at a time, but they are all sold out due to high demand. It kind of makes sense since their history is only supporting the high end GPUs in their HPC solutions, where they don't use VM's. They've committed to us directly that they will fix this issue. I updated our pricing page to note this.
        trogdor 2y ago
        >I updated our pricing page to note this Nice job on your website! Sincerely, Someone who recently gave you a hard time for not having info on your website.
karma_pharmer 2y ago
Or they're trying to distract attention from the fact that they've already sold out 100% of the fab capacity available to produce these chips for the next two years. So really, they lose nothing. They've already booked sales of everything there is to sell. So might as well now turn attention to those who might be customers two years from now, and make them feel like the wait will be worth it.

Netcob 2y ago

In AI, that doesn't sound too surprising to me right now. I just experiment with some local LLMs, but the differences are pretty huge: Llama 3 8B, Raspberry Pi 5: 2-3 Tokens/second (but it works!) Llama 3 8B, RTX 4080: ~60 Tokens/second Llama 3 8B, groq.com LPU, ~1300 Tokens/second Llama 3 70B, AMD 7800X3D: 1-2 Tokens/second Llama 3 70B, groq.com LPU, ~330 Tokens/second There seem to be huge gaps between CPU, GPU and specialized inference ASICs. I'm guessing that right now there aren't many genius-level architecture breakthroughs, and that it's more about how much memory and silicon real estate you're willing to dedicate to AI inference.

SushiHippie 2y ago
What quantization levels did you use? I think groq doesn't use quantization, so the gap between your hardware and groq would be even further apart.
- kkielhofner 2y ago
  > I think groq doesn't use quantization, so the gap between your hardware and groq would be even further apart. To my knowledge this isn't (absolutely) publicly known but users on /r/LocalLLaMA and elsewhere have provided some pretty clear examples that Groq is almost certainly quantized. Which makes sense considering their memory situation... An entire GroqRack (42U cabinet) has 14GB of RAM which means it likely can't even reasonably run llama3 8b in BF16/FP16. Let alone 70b, Mixtral, etc. The amount of hardware required to run their public-facing hosted product likely takes up an obscene amount of floor space, even in int4. Their docs for GrowFlow describe int8 quantization but their toolkit is heavily dependent on ONNX, which has had recent tremendous work in terms of different post training quantization strategies and precisions. However, the power efficiency vs performance is very good, potentially to the point of being able to use very cheap datacenter/co-location space that isn't capable of meeting the power and (air) cooling densities of datacenter AMD and Nvidia GPU products. Interestingly I have access to a GroqRack system that I'm hoping to be able to spend some time on this week.
  - SushiHippie 2y ago