Nvidia L40S is a Nvidia H100 AI alternative | Hacker News Reader

Nvidia L40S is a Nvidia H100 AI alternative

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51 pointsPaulHoule27 comments

Looking back to the recent 'semianalysis' article on NVIDIA: https://www.semianalysis.com/p/nvidias-plans-to-crush-compet...

'For those OEMs to win larger H100 allocation, Nvidia is pushing the L40S. Those OEMs face pressure to buy more L40S, and in turn receive better allocations of H100.'

Lots of articles and opinions popping out about the L40S as a serious H100 alternative at the same time. The people buying these kinds of cards have already been looking seriously (10-12k easy to source against 35k impossible to get?) and rejected them because for training memory bandwidth, intranode (nvlink) and internode (pcie5 and... Nvlink too?) interconnect, and tensor core performance are critical.

Now if your application is fp32 and compute bound (or mostly compute bound) or if you can make it fly with cuda cores and bit of tensor cores, a bit of work yes, and you're still on Eth200G (so pcie 4 is OK) the L40 is an incredible beast and you can build incredibly dense compute nodes with up to 700 TFLOPS which is just mad.

So it looks like something good for fluid dynamics, or maybe 3D rendering, but not ML, correct?

Yeah, smells like a "Simulation Card".

Real-time or offline signal processing, data analytics, image/video processing, non-huge-network ML. With 90 TFLOPS FP32 and 48GB GDDR6 (albeit hard to harness) there's a huge lot you can do in computing that is neither simulation nor LLM AI Training.

Note this is RTX 4090 silicon.

In inherits the... quirks of the 4090, namely the loss of NVLink (which the 3090 had) and basically the same memory bandwidth as the 3090. Also, apparently the crippled tensor core performance for the L40. And the price hikes.

I'm not really trying to sound bitter, but its hard to talk about Nvidia's lineup now while ignoring the artificial segmentation or price inflation and some small corner cutting. They would make Intel at their anti-competitive peak blush.

And don't forget the damned PCIe 4. In 2023.

Was the L40S intended to be a workaround for the export restrictions on the H100, or did Nvidia always plan to create the L40S?

https://fortune.com/2023/11/01/nvidia-shares-fall-report-us-...

>The restrictions were supposed were supposed to only come into play on Nov. 17, 30-days after the US first announced it. But in a filing on Oct. 24, Nvidia said it was informed that the rules were effective immediately and that it would affect shipments of Nvidia’s A100, A800, H100, H800, and L40S products. The 800 series chips were designed specifically for the Chinese market to circumvent the earlier iterations of the export control rules.

> did Nvidia always plan to create the L40S

Nvidia repurposes their gaming silicon every generation, and this one is no different.

If you are talking about the L40 vs L40S, I'm not sure. Does the L40 just barely sneak by the export restrictions?

I don't think L40/L40S are allowed to be exported when all the other AD102 (the underlying silicon) variants are banned including lower specced ones like 4090s.

> exceeding certain performance thresholds (including but not limited to the A100, A800, H100, H800, L40, L40S, and RTX 4090).

Seems like it's included in the SEC filing[0]

[0]: https://www.sec.gov/ix?doc=/Archives/edgar/data/1045810/0001...

L40 throughput is technically lower though since it isn't clocked to the stratosphere like the 4090. I was wondering if it was barely at the threshold or something.

EDIT: Ah interesting, so it is included.

L40S sound good on paper, but the memory bandwith compared to even a 40G A100 is reduced in half which is crazy in the era when we are bottlenecked by it rather than the actual compute. It costs as much (or even more) than an 80G A100, but instead of getting ~2TB/s, you get ~800GB/s.

L40S might be a more appealing choice if you are switching from an A40, but then why the hell aren't you using A100 which is actually much cheaper and much more commonly available. The only perceivable reason I can see is fp8 support, but even with it, I don't think it is worth the price.

Your statement is only partially correct. For single batch inference, something we do locally, the bottleneck is generally bandwidth specially for llm. But no one does single batch inference in server. Also training is bottlenecked by compute and not memory bandwidth.

I could really use some advice for making some deep learning hardware purchases for my university lab.

It has been a pretty painful experience speaking to the companies due to shortages and because of all the limitations of non-H100 chips. For getting a machine with 4 H100s and NVLINK, I was told I'd have to wait one year. With that long of a wait, I might as well wait for the next generation. Without NVLINK, I could potentially get this machine sooner. I struggled to find any reports of the additional complexities and how much slower the machine would be.

I'm basically looking to buy two machines. One for under $40K and one for up to $100K. The $100K machine is intended for training LLMs, and the other would have multiple GPUs where the other would just have a bunch of GPUs for my PhD students to use. For the $40K machine, I'm being told by companies I could only put in 2-3 L40S cards, and they are really pushing these as the only viable option.

In contrast, the last time I had $40K for hardware I got two machines with 4 RTX A5000s and companies were a lot more responsive and seemed more helpful. It is unlikely that I'll have $100K for hardware again, so I'm very reluctant to go with cloud computing since that decreases my hardware budget and I need these to last 3+ years.

I wish I could go with 4090s, but the limited VRAM and that NVIDIA has disabled functionality needed for multi-GPU training makes that pretty much a non-starter.

We had the same issue for our lab when we were spec'ing up a similar install. The one thing I didn't fully realise is the need of having an Enterprise subscription to run the A100s, H100s and other cards. The drivers for these cards are behind a paywall and for academia it looks like it's around $150 per card, per year to run (if you want to run in a DC - https://www.nvidia.com/content/dam/en-zz/Solutions/design-vi... ). We bought 3, A100s for the servers and 3 L40's (not L40S) this was limited due to space in the severs. The NVLINKs can be added later (it's just a bridge between the cards) so if you can get them without NVLINK I would. (NVLINK works well with cards stacked vertically rather than horizontally). We also bought a desktop system with dual 4090s (£15k - $18.5k) to start on smaller models before scaling up on our servers with the real grunt work happens. This worked well as many problems can be solved with smaller models before going all out needing 4 H100s. Hope this helps with the planning. Feel free to reach out if you want to chat more about our setup. (My HackerNews Username is the same as github and you'll find my email address there)

I don’t know about the L40S but my experience with the L40 hasn’t been so good. I would say it’s okay-ish to decent for inference. Horribly slower than a H100 or even a A100 for training.

The FP8 and higher cuda level is nice I guess, if your toolchain supports it.

You just perfectly summarize the article.

L4s are the only newer GPUs that you can get on GCP(g2) while A10 in AWS in the 1x config(g5), H100s are only in 8x variant otherwise.

Lambdalabs if you're lucky will grant 1x H100 at $2/hr :)

Every other serverless provider charges 3-5X the typical hourly cost.

I use L40s because H100s and A100s are backlogged, but for big models the lack of memory capacity and PCIe card-communication bottleneck is a pain, I'm planning to switch as soon as there's capacity available

Did I miss it or did they not say how much it costs?

https://www.amazon.com/dp/B0CLTDCZ82

$13,744.95

For that price I'd expect more memory.

48GB is the most Nvidia can give the silicon.

I think AMD could theoretically make a 96GB inference card this gen with their modular memory controller...

AMD offers 128GB on their current Mi250 line and the upcoming Mi300x is said to offer 192GB per card.

>128GB on their current Mi25

This is 2xGPU, and two very expensive GPUs with HBM.

I was thinking they use the 7900 XTX die, and swap out the tiny memory controller dies or somehow double them up (which is infinitely cheaper than redoing the whole big die). They could massively undercut the L40 in cost while still doubling up the vram to 96GB, and doubling the bandwidth as a happy side effect.

$12k each at Thinkmate

For those that want to see where you can try an L40 in the cloud checkout: https://gpumonger.com/gpu/l40#providers

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