Lambda Labs Blog

Lambda | Linux Technical Support Specialist | San Francisco, CA (Dogpatch) | Onsite | Full Time | https://lambdalabs.com We're looking for a linux technical support specialist who wants to help provide our customers with world class UNIX/Linux support for their GPU servers, workstations, laptops, and cloud instances. * Be first point of contact for all incoming technical support questions (phone, email, chat, and forum) * Troubleshoot software and hardware issues for customers * Document solutions to common problems Required Experience: * Linux background, hobbyist or professional * Shell and Python scripting proficiency * Excellent written and oral communication skills === We're located in San Francisco 5 minutes away from the 22nd street Caltrain. Apply to: https://lambdalabs.com/careers

Lambda Labs|Director of Marketing|Remote|https://lambdalabs.com/ About the Role: Lambda provides GPU infrastructure to the world’s leading AI labs. As our Director of Marketing you’ll lead everything from product marketing to demand generation to reach more AI engineers and researchers. Our business has grown by more than 10x over the last few years and we’re looking for a marketing leader who can help take us through our next stage of growth. Reporting to our CEO, you’ll have complete end to end ownership of our digital ad campaigns, setting our marketing strategy, budget, and generating marketing qualified leads. About You: Strong technical marketing background, ideally in enterprise software or hardware. Years of experience managing large demand generation budgets and crafting. impactful product messaging. Proven management experience. Experience We're Looking For: -5+ years of experience in a management role. -Extensive experience targeting a hardware and/or SaaS audience. -Startup experience & mindset with a bias for action. -Metrics and data-driven approach. -Able to operate independently and cross functionally to build out new programs and initiatives. -Deep understanding of CRM, marketing automation, digital marketing and email marketing tactics and tools. -Excellent written and verbal communication skills. -BS EECS/Math or previous work experience in an engineering position. https://boards.greenhouse.io/lambda/jobs/4372122003 Contact:Charles…

I'm not sure I follow. Knowledge based retrieval of what exactly? Outdated docs and dilapidated code? Aging internal wikis and chat histories erased for legal reasons? Everyone also seems to overlook how much time and resources it takes for these models to be trained/fine tuned on a corpus of knowledge. Researches have calculated it probably took OpenAI the equivalent of 355 years on a single NVidia V100 to train up GPT 3. [1] Clearly they used more horsepower in parallel, which is a foreseen problem right now for other reasons. [2] [1] https://lambdalabs.com/blog/demystifying-gpt-3 [2] https://www.pcworld.com/article/2020375/the-ai-boom-could-create-a-new…

Looks like their site is quoting a rate of $1.99 now https://lambdalabs.com/

Lambda | Full-Time | Software Engineers | Remote US & Canada | https://lambdalabs.com/ We are looking for talented software engineers to join our team. We're currently hiring for multiple engineering positions and more. Lambda is a fast growing startup providing deep learning hardware, software, and cloud services to the world's leading companies and research institutions. Lambda’s mission is to create a world where access to computation is as effortless and ubiquitous as electricity. Learn more about our team and open roles here: https://lambdalabs.com/careers If you are interested, you can reach me at austin(at)lambdal.com (mention HN in the subject line)

Lambda has a very interesting benchmark page https://lambdalabs.com/gpu-benchmarks If you look through the throughput/$ metric, the V100 16GB looks like a great deal, followed by H100 80GB PCIe 5. For most benchmarks, the A100 looks worse in comparison

On equivalent instances it seems a good bit more expensive than e.g. lambdalabs [1], but in turn you get a lot more flexibility. Both in terms of having more GPU models to choose from, and having much more flexible machine configurations. Seems like a worthwhile tradeoff. 1: https://lambdalabs.com/service/gpu-cloud#pricing https://oblivus.com/pricing/

It's best to choose something you personally find interesting, for example, I'm interested in audio generation, so I'd pick some papers that describe a music/voice generation model or algorithm, but to you it might be something completely different. When you do decide on a paper, take a look at Phil Wang's implementation style: https://github.com/lucidrains?tab=repositories, he has hundreds of papers implemented. If you don't already have a GPU machine, you can rent 40GB A100 instance for $1.1/hr or 24GB A10 for $0.6/hr: https://lambdalabs.com/service/gpu-cloud.

Lambda is great, if you can afford it: https://lambdalabs.com/service/gpu-cloud/pricing

I just went this route to build a ML workstation. I was able to get a prebuilt with a 3090 for ~30% of a comparable build on https://lambdalabs.com/. Now I just need to wait until next year to get the second 3090. I considered waiting for the new A4000s to launch to see if they'd have better inventory but figured 3090s have additional value as a gaming gpu. I'd be curious how professional ML folks are fairing and what they're using -- it's not too late to cancel my build.

https://lambdalabs.com/blog/demystifying-gpt-3/ According to ^, that's about 11 GPT-3s trainings worth in the cloud.

GPT-3 was another buckets worth of evidence in favor of the scaling hypothesis. Performance kept improving (and cost to train kept increasing) as more parameters were added. Even with 175 billion parameters, the performance had not yet plateaued. One take-away is that throwing a lot of compute at the problem helps tremendously :). You can read more about GPT-3 here: https://lambdalabs.com/blog/gpt-3/

If you're not deploying in a datacenter, you can save even more money by building a workstation with a few 2080 Ti cards, which cost $1200 and give 90% of the speed of the $3000 Titan V: https://lambdalabs.com/blog/best-gpu-tensorflow-2080-ti-vs-v100-vs-tit…

Interesting note about Lambda Labs, all of the press links on https://lambdalabs.com/?ref=blog are about a ~"privacy violating Google Glass app" that recognizes faces and geotags photos of them. I don't see why they choose to promote that link now.

you can use lambda? https://lambdalabs.com/

I'm waiting for a build so I went fully down this trail. XD A1) Yes it can be done with a 4090. (2a). A2) 2 days. (4d/4e). B) Up to you, author did both and settled on 50M which they got to 1300 ELO. (note: they also did subsequent work with the same model to increase perf without further training) (2a). C) Mu: there's no limit as you can page in/out (3a, 3c), and its trivial to store in both RAM and VRAM. 30B fits in memory with 32 GB of RAM or VRAM. (3e) Starting from: 1. article: https://adamkarvonen.github.io/machine_learning/2024/03/20/chess-gpt-i… 2. second sentence links to post on training: https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world… 2a. "A 50 million parameter GPT trained on 5 million games of chess learns to play at ~1300 Elo in one day on 4 RTX 3090 GPUs" 2b. "The 50M parameter model played at 1300 Elo with 99.8% of its moves being legal within one day of training" 3. re: most parameters 4090 can do: 3a. understanding is there is no limitation, in that, you don't _need_ to have every parameter in memory at all times, either during training or inference. 3b. google "are amount of parameters in llm limited by vram size" 3c. go to /r/LocalLLaMa link: https://www.reddit.com/r/LocalLLaMA/comments/15j0mvm/what_are_the_vari… (why? its a favorite of mine, believe its the closest you get to local training ppl talking in open space that's not discord) 3d. understanding in 3a…

How does this compare to https://lambdalabs.com/ ?

Lambda Labs crunched the numbers in Feb 2022 [0]. They concluded: “”” So, which GPUs to choose if you need an upgrade in early 2022 for Deep Learning? We feel there are two yes/no questions that help you choose between A100, A6000, and 3090. These three together probably cover most of the use cases in training Deep Learning models: Do you need multi-node distributed training? If the answer is yes, go for A100 80GB/40GB SXM4 because they are the only GPUs that support Infiniband. Without Infiniband, your distributed training simply would not scale. If the answer is no, see the next question. How big is your model? That helps you to choose between A100 PCIe (80GB), A6000 (48GB), and 3090 (24GB). A couple of 3090s are adequate for mainstream academic research. Choose A6000 if you work with a large image/language model and need multi-GPU training to scale efficiently. An A6000 system should cover most of the use cases in the context of a single node. Only choose A100 PCIe 80GB when working on extremely large models “”” [0] https://lambdalabs.com/blog/best-gpu-2022-sofar/

Thank you for cross-referencing that, the data does look accurate and my statement now seems exaggerated. I do think we need skepticism on the A100 charts though until third party benchmarks. > In my experience NVidia benchmark numbers in deep learning are rarely lies - they are highly optimised, in optimal conditions and rarely achievable in the real world. Right, but Nvidia claimed 1360 images/sec for resnet-50 on imagenet. To my knowledge this still hasn’t been realized by a third party. It also isn’t a 4x improvement for fp32 vs fp16, that’s comparing to previous generation. Improvement is more like 1.5x: https://lambdalabs.com/blog/best-gpu-tensorflow-2080-ti-vs-v100-vs-tit… Even in very simple synthetic benchmarks the speed up is only 2x: https://github.com/tensorflow/benchmarks/issues/77#issuecomment-349838… I have not seen any benchmarks showing an 8x speedup. Have you? If not -> Nvidia lied.

Check out LambdaLabs Inference: https://lambdalabs.com/inference It's pretty quick and has the open models. They have Qwen right now, I wouldn't be surprised if they added Deepseek soon.

Hot Aisle: https://hotaisle.xyz/pricing/ Lambda: https://lambdalabs.com/service/gpu-cloud#pricing

You're doing inference, not training. https://lambdalabs.com/gpu-benchmarks

Out of curiosity, looking at the cheapest price for a H100 that I could find online. Lambda Reserved Cloud [1] starts at $1.89 per H100 per hour. It could be possible to get the cost down to a lower amount: $1.89 * 96GPUs * 24hours * 14days = ~$61k 1 - https://lambdalabs.com/deep-learning/servers/hyperplane

I agree. Availability is a pain in the ass which might a dealbreaker for urgent interactive use cases but a 48GB A6000 on LambdaLabs is $0.80/hr [1]. A newer 80GB H100 is $1.99/hr so especially if you're trying to do batch processing and can script a bot to wait for availability, it's often a much better option. With that aforementioned A6000 ($5k retail) you'd have to use it for at least six thousand hours to break even on the cloud cost. [1] https://lambdalabs.com/service/gpu-cloud#pricing

"But to put things into perspective, GPT-3 175B model required 3.14E23 FLOPS of computing for training. Even at theoretical 28 TFLOPS for V100 and lowest 3 year reserved cloud pricing we could find, this will take 355 GPU-years and cost $4.6M for a single training run." https://lambdalabs.com/blog/demystifying-gpt-3/

That section of the text is ambiguous but it's $250k for 8 x H100s (PCI-e) and $350k for 8 x H100 (SXM). The prices above are plus or minus from Lambda Labs[1]. [1]: https://lambdalabs.com/products/scalar

I'm not OP/submitter, but the main differentiator is that Lambda doesn't have on-demand availability for lots of interlinked H100s - you have to reserve them. Lambda has "Lambda Sprint" which is kinda similar,[1] but Sprint is $4.85/GPU/hr instead of <$2. So if you want 128 GPUs for a week, you can't use Lambda reserved (3 year term), you can't use Lambda on-demand (can't get 128 A/H100s on-demand), your options are Lambda Sprint or SF Compute, and SF Compute is offering significantly lower prices. [1]: https://lambdalabs.com/service/gpu-cloud/reserved

Well at least in the U.S. a lot of used 3090s are out for less than $800 on eBay which is a steal for 24GB VRAM. The 4090 is about $1600 and it's still a consumer-level card; gamers are buying it more often than the 4080. For ML workloads, it's actually a pretty good deal considering how expensive Nvidia's higher-end, non-consumer offerings are. Lambda Labs did some benchmarking: https://lambdalabs.com/blog/nvidia-rtx-4090-vs-rtx-3090-deep-learning-… But yeah the M2 MacBooks are incredible for local LLMs for their price. Nvidia doesn't have any consumer-level priced accelerators with that much memory.

Here's an example of an 8x H100 machine - look at the tech specs: https://lambdalabs.com/deep-learning/servers/hyperplane 6x 3000W PSUs in a 3x3 redundant config. So 9000 watts total. So at least 240v x 50A. x2 for redundancy.