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Comment by moffkalast | Hacker News Reader

It's not so much an accelerator as it is addressing the main inference bottleneck (i.e. memory latency) with sheer brute force by throwing money at the problem. They've made accelerators out of pure L3 cache with a whopping 230 MB per card. They cited something like 500 cards to load one single Mixtral instance, which probably cost over $10M to build. It's a supercomputer essentially.

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Or to put it another way: they’ve made a compute substrate with the correct ratios of processing power to memory capacity.

NVIDIA GPUs were optimised for different workloads, such as 3D rendering, that have different optimal ratios.

This “supercomputer” isn’t brute force or wasteful because it allows more requests per second. By having each response get processed faster it can pipeline more of them through per unit time and unit silicon area.

The correct ratio for one workload (production inference).

A recent presentation on the architecture

https://youtu.be/WQDMKTEgQnY?si=W0E9Kq6P280l3Wcl

IMO we still need an MLPerf submission or similar to really understand if this is more efficient or more efficient only if you also want to minimize latency.

Nvidia has pulled enough rabbits out of the hat when it comes to MLPerf I’m still not convinced they can’t work some CUDA magic and undercut them on efficiency.

> pure L3 cache with a whopping 230 MB per card

Just to put these numbers in perspective a desktop 8 core 7800x3d has 96MB of L3 cache, and the top-end 96-core Epyc 9684X has 1.15GB of L3.

They need 568 LPUs to load both Mixtral 8x7B and LLaMA 70B, because they need both those models available for the demo.

I imagine Mixtral by itself would only take something like 200-300 LPUs

Only $5M then.

$5M once, upfront. But given the significantly increased throughput, how fast does that pay for itself?

Depends on power usage. I’m curious how power hungry those are compared to server/workstation cards.

You need computers for all of them and megawatts of power, power supplies, cooling, and power distribution.

Naturally, but you need that for GPUs as well, no? What is the actual difference when running, when measured per token generated?

I'm pretty sure $20,000 per LPU isn't actually the cost of these LPUs. I saw someone else on HN asking if $20,000 could get them something and an employee said to reach out. Which makes me think $20,000 is enough to get some sort of model running at least, even if it's not necessarily an LLM.

What's the cost per inference relative to H100? Isn't that the number to care about?

If you believe the marketing material it’s lower. Their API is the cheapest around, so either it’s true or they’re subsidizing.

Another consideration: Even if it's slightly more expensive, that can be OK if you care about inference speed. I'd pay 50% more for GPT-4 if it could deliver results that quick.

Based on some rough ballpark conservative estimates (one server with 2 A100 at $50000; 50 tokens/s one one of those servers; so 10 of those servers), upfront cost with consumer hardware seems to be 1/10 to 1/20 of what the Groq hardware costs. I would guess that realistically cloud providers can probably achieve half to 1/3 of that price

So unless you need the fast latency of Groq, consumer hardware seems to be a lot cheaper for the same thoughput.

Grayskull has 96 MB SRAM and people call it overpriced at $600 to $800. It is far more plausible that their chip costs are somewhere around $500.

Nothing wrong with that, though.

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