←back to thread

Llama 3.1 405B now runs at 969 tokens/s on Cerebras Inference

(cerebras.ai)
426 points benchmarkist | 8 comments | 19 Nov 24 00:15 UTC | HN request time: 0.43s | source | bottom
Show context
zackangelo ◴[19 Nov 24 02:11 UTC] No.42179476[source]▶
This is astonishingly fast. I’m struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.

I’m curious how they’re doing it. Obviously the standard bag of tricks (eg, speculative decoding, flash attention) won’t get you close. It seems like at a minimum you’d have to do multi-node inference and maybe some kind of sparse attention mechanism?

replies(9): >>42179489 #>>42179493 #>>42179501 #>>42179503 #>>42179754 #>>42179794 #>>42180035 #>>42180144 #>>42180569 #
danpalmer ◴[19 Nov 24 02:17 UTC] No.42179501[source]▶
Cerebras makes CPUs with ~1 million cores, and they're inferring on that not on GPUs. It's an entirely different architecture which means no network involved. It's possible they're doing this significantly from CPU caches rather than HBM as well.

I recommend the TechTechPotato YouTube videos on Cerebras to understand more of their chip design.

replies(3): >>42179509 #>>42179599 #>>42179717 #
accrual ◴[19 Nov 24 03:02 UTC] No.42179717[source]▶
I hope we can buy Cerebras cards one day. Imagine buying a ~$500 AI card for your desktop and having easy access to 70B+ models (the price is speculative/made up).
replies(4): >>42179769 #>>42179834 #>>42180050 #>>42180265 #
danpalmer ◴[19 Nov 24 04:13 UTC] No.42180050[source]▶
I believe pricing was mid 6 figures per machine. They're also like 8U and water cooled I believe. I doubt it would be possible to deploy one outside of a fairly top tier colo facility where they have the ability to support water cooling. Also imagine learning a new CUDA but that is designed for another completely different compute model.
replies(5): >>42180442 #>>42180470 #>>42180527 #>>42181229 #>>42181357 #
trsohmers ◴[19 Nov 24 06:21 UTC] No.42180527[source]▶
Based on their S1 filing and public statements, the average cost per WSE system for their (~90% of their total revenue) largest customer is ~$1.36M, and I’ve heard “retail” pricing of $2.5M per system. They are also 15U and due to power and additional support equipment take up an entire rack.

The other thing people don’t seem to be getting in this thread that just to hold the weights for 405B at FP16 requires 19 of their systems since it is SRAM only… rounding up to 20 to account for program code + KV cache for the user context would mean 20 systems/racks, so well over $20M. The full rack (including support equipment) also consumes 23kW, so we are talking nearly half a megawatt and ~$30M for them to be getting this performance on Llama 405B

replies(5): >>42180544 #>>42181290 #>>42181897 #>>42181931 #>>42190965 #
danpalmer ◴[19 Nov 24 06:25 UTC] No.42180544[source]▶
Thank you, far better answer than mine! Those are indeed wild numbers, although interestingly "only" 23kw, I'd expect the same level of compute in GPUs to be quite a lot more than that, or at least higher power density.
replies(1): >>42180615 #
1. YetAnotherNick ◴[19 Nov 24 06:42 UTC] No.42180615[source]▶
You get ~400TFLOP/s in H100 for 350W. You need (2 * token/s * param count) FLOP/s. For 405b, 969tok/s you just need 784 TFLOP/s which is just 2 H100s.

The limiting factor with GPU for inference is memory bandwidth. For 969 tok/s in int8, you need 392 TB/s memory bandwidth or 200 H100s.

replies(3): >>42182416 #>>42182750 #>>42190957 #
2. latchkey ◴[19 Nov 24 11:49 UTC] No.42182416[source]▶
Memory bandwidth and memory size. Along with power/cooling density.

Hence why you see AMD's MI325x coming out with 256GB HBM3e, but it is the same FLOPs as a 300x. 6TB/s too, which outperforms H200's, by a lot.

You can see the direction AMD is going with this...

https://www.amd.com/en/products/accelerators/instinct/mi300/...

3. Const-me ◴[19 Nov 24 12:35 UTC] No.42182750[source]▶
> For 969 tok/s in int8, you need 392 TB/s memory bandwidth

I think that math is only valid for batch size = 1. When these 969 tokens/second come from multiple sessions of the same batch, loaded model tensor elements are reused to compute many tokens for the entire batch. With large enough batches, you can even saturate compute throughput of the GPU instead of bottlenecking on memory bandwidth.

replies(1): >>42190945 #
4. ryao ◴[20 Nov 24 04:58 UTC] No.42190945[source]▶
They claim to obtain that number with 8 to 20 concurrent users:

https://x.com/draecomino/status/1858998347090325846

5. ryao ◴[20 Nov 24 05:01 UTC] No.42190957[source]▶
Memory bandwidth for inferencing does not scale with the number of GPUs. Scaling instead requires more concurrent users. Also, I am told that 8 H100 cards can achieve 600 to 1000 tokens per second with concurrent users.
replies(1): >>42193142 #
6. YetAnotherNick ◴[20 Nov 24 12:07 UTC] No.42193142[source]▶
8 H100 could achieve lot more than 1000 token/sec.

> Memory bandwidth for inferencing does not scale with the number of GPU

It does

replies(1): >>42197442 #
7. ryao ◴[20 Nov 24 19:50 UTC] No.42197442{3}[source]▶
This is on llama 3.1 405B.

Inferencing is memory bandwidth bound. Add more GPUs on a batch size 1 inference problem and watch it run no faster than the memory bandwidth of a single GPU. It does not scale across the number of GPUs. If it could, you would see clusters of Nvidia hardware outperforming Cerebras’ hardware. That is currently a fantasy.

replies(1): >>42200967 #
8. YetAnotherNick ◴[21 Nov 24 04:01 UTC] No.42200967{4}[source]▶
This two sources[1][2] shows 1500-2500 token/per second on 8*H100.

[1]: https://lmsys.org/blog/2024-07-25-sglang-llama3/?ref=blog.ru...

[2]: https://www.snowflake.com/engineering-blog/optimize-llms-wit...