Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr (oblivus.com)
This is Doruk from Oblivus, and I'm excited to announce the launch of our platform, Oblivus Cloud. After more than a year of beta testing, we're excited to offer you a platform where you can deploy affordable and scalable GPU virtual machines in as little as 30 seconds! https://oblivus.com/cloud
- What sets Oblivus Cloud apart?
At the start of our journey, we had two primary goals in mind: to democratize High-Performance Computing and make it as straightforward as possible. We understand that maintaining GPU servers through major cloud service providers can be expensive, with hidden fees adding to the burden of running and maintaining servers. Additionally, the cloud can sometimes be overly complex for individuals who don't have much knowledge but still require powerful computing resources. That's why we decided to create a platform that offers affordable pricing, easy usability, and high-quality performance.
- Features
1. Fully customizable infrastructure that lets you switch between CPU and GPU configurations to suit your needs.
2. Transparent and affordable per-minute-based Pay-As-You-Go pricing with no hidden fees. Plus, free data ingress and egress. (Pricing: https://oblivus.com/pricing/)
3. Optimized cost with storage and IP address-only billing when the virtual machine is shut down.
4. Each virtual machine comes with 10Gbps to 40Gbps public network connectivity.
5. NVMe ($0.00011/GB/hr) and HDD ($0.00006/GB/hr) storage that is 3x replicated to fulfill your storage needs.
6. Choose from a variety of cutting-edge CPUs and 10 state-of-the-art GPU SKUs. (Availability: https://oblivus.com/availability/)
7. OblivusAI OS images come with pre-installed ML libraries, so you can start training your models right away without the hassle of installing and configuring the necessary libraries.
8. If you're working with a team, utilize our organization feature to simplify the billing process. Everyone in your organization uses the same billing profile, so you don't need to keep track of multiple accounts.
9. No quotas or complex verification processes. Whether you represent a company, an institution, or you're a researcher, you have full access to our infrastructure without any limitations.
10. Easy-to-use API with detailed documentation so that you can integrate your code with ours.
- Pricing
At Oblivus Cloud, we provide pricing that is affordable, transparent, and up to 80% cheaper than major cloud service providers. Here is a breakdown of our pricing:
1. CPU-based virtual machines starting from just $0.019/hour.
2. NVIDIA Quadro RTX 4000s starting from $0.27/hour.
3. Tesla V100s starting from $0.51/hour.
4. NVIDIA A40s and RTX A6000s starting from $1.41/hour.
We also offer 6 other GPU SKUs to help you accurately size your workloads and only pay for what you need. Say goodbye to hidden fees and unpredictable costs.
If you represent a company, be sure to register for a business account to access even better pricing rates.
- Promo Code
Join us in celebrating the launch of Oblivus Cloud by claiming your $1 free credit! This may sound small, but it's enough to get started with us and experience the power of our platform. With $1, you can get over 3 hours of computing on our most affordable GPU-based configuration, or over 50 hours of computing on our cheapest CPU-based configuration.
To redeem this free credit, simply use the code HN_1 on the 'Add Balance' page after registration.
Register now at https://console.oblivus.com/register
- Quick Links
Website: We often receive comparisons to Lambda, but it ultimately boils down to your specific requirements and preferences. As you mentioned, our platform is tailored for personalized virtual machine configurations, distinguishing us from other cloud service providers that offer pre-set configurations. We strongly believe that this level of flexibility is a key differentiator and delivers enhanced value to our customers. Given how easy it is to shoot oneself in the foot with stolen credentials or bad Terraform code, I'd think it should be possible for users to set their own quota that is locked for, say, 24 hours to at least put a cap on abuse scenario costs. Just wanted to clarify; currently, our platform operates on a pre-paid system where users are required to make a deposit to initiate server usage. We do offer the option for users to deposit smaller amounts, starting from $5, which helps minimize potential losses in the event of stolen credentials. Additionally, we have an Auto Top-Up system that can automatically replenish your balance based on the settings you specify. We also send notification emails to ensure that any transaction made is intentional and authorized. As a result, even if you have full access to our platform, you would need an account balance to deploy or maintain virtual machines. But we genuinely appreciate your suggestion and will give serious consideration to incorporating such a security feature into our platform. Thank you once again for your valuable input! In the Comparison table, some of the savings percentages seem to be calculated incorrectly, as the percentage of the price left instead of the percentage of the price saved (e.g. 3.99/5.88 instead of (5.88-3.99)/5.88). The pricing is transparent but complicated. I think you need calculator that lets you select different options and tells you the price. Maybe you have such a thing? I didn't find it. We offer two types of virtual machines: GPU-based and CPU-based. The pricing structure differs slightly for each. For GPU-based VMs: The vCPU/GPU and RAM/GPU ratio is set at a minimum of 1-to-1. This means that if you deploy a VM with one GPU, you will need at least 1 vCPU and 1GB of RAM. As our platform is fully customizable, we bill you for the specific amount of RAM ($0.006/hr per GB) and the number of vCPUs ($0.011/hr per vCPU) you select. For CPU-based VMs: The RAM/vCPU ratio is fixed at 4. In this case, only the vCPU is customizable, and you will only be billed for the number of vCPUs you choose. The cost of RAM is already included in the price you pay for the vCPU. Here's an example: A GPU-based VM with 1x Quadro RTX 4000 ($0.27/hr), 1 vCPU ($0.011/hr), 1GB of RAM ($0.006/hr), and a 40GB NVMe drive (40 x $0.00011/hr). Another example: A CPU-based VM with 1x AMD EPYC Rome ($0.033/hr), 4GB of RAM (FREE/INCLUDED), and a 40GB NVMe drive (40 x $0.00011/hr). We also have a calculator available on the VM deployment page, where you can see detailed information about the specific configuration you want to deploy. Once you register on our console, you can access the calculator at https://console.oblivus.com/dashboard/oblivuscloud/deploy/. Please note that I have forwarded the comparison calculations internally, and they should be fixed soon. Thank you for bringing it to our attention! Remember retail GPUs are much more powerful, much cheaper and much more available than cloud GPUs. Just go to the store and buy one. Acquiring the latest technology, such as an A100 GPU, individually from a store can indeed be quite expensive, with prices around $10,000 per unit. Additionally, setting up and maintaining a home lab to scale from one GPU to thousands can be a significant investment in terms of both cost and resources. In contrast, our platform provides access to the latest GPU technology without the need for users to individually purchase and manage the hardware. We offer the scalability to deploy multiple GPUs as needed and scale back down when required, making it a more cost-effective and flexible solution compared to setting up and maintaining a personal lab. Furthermore, it's important to note that our GPUs are directly dedicated to each virtual machine, ensuring that the power and performance are not compromised by sharing resources. This ensures that our GPUs provide the full capability and performance expected, making them just as powerful as individual units. With the 4090 being a fair amount more powerful, it might give the a100 a challenge for about 10% of the cost. That’s still about 10k hours of renting one with this service though. [1]: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Cent... [2]: https://images.nvidia.com/aem-dam/Solutions/Data-Center/l4/n... If you have conducted your tests on shared instances and have observed such differences, then it's understandable. However, I want to emphasize once again that we provide dedicated virtual machines, which means that the resources are exclusively allocated to each user and not shared. At least in the past there was a tendency by NVIDIA to run their professional GPUs at a lower frequency, offering reliability and correctness guarantees as tradeoff. Meanwhile most of the retail/gaming GPUs came overclocked and almost outright guaranteeing that they would start to crash and fail if you tried to run them 24/7. Our intention is not to discourage anyone from purchasing their own GPUs. We offer our GPU cloud services as an alternative for those who may not have the resources or prefer the convenience and flexibility of renting GPUs on-demand. We apologize if the discussion took a different direction, and we appreciate your understanding. So the consumer would care because the service is lower cost. It's more expensive to rent a GPU than to buy it. This whole comment thread started because GP wrote: >Remember retail GPUs are much more powerful, much cheaper and much more available than cloud GPUs. So, still, why would a consumer care about performance per watt? (ofc assuming you're not going to use it for like a day) And GPU-GPU interconnects are important for this type of training so putting as many GPUs as possible close together is necessary. Now, "running" may also mean different things. For example, you may want to do things s.a. performance diagnostics (in order to understand if your code uses resources efficiently), and then you'd need stuff like NVML, DCGM and co. Consumer-grade hardware might not be supported, or might not in principle support diagnostics collection / instrumentation. Or, if you have multiple GPU-dependent workloads that cannot saturate your resources -- you might think of MIG as being a way to address that... and, again, consumer-grade GPUs won't help you here... I'm not saying you shouldn't try self-hosting. I'm actually all for it. But, you also need to be mindful of the pros and cons. NVidia must have some reason to pitch the datacenter family of GPUs to, well, datacenters. They aren't just blowing up prices. Also, to give some sense of comparison: Titan X ~= 3.5K CUDA cores. V100 ~= 5K CUDA cores. A100 ~= 7K CUDA cores. H100 ~= 18.5K CUDA cores. It probably doesn't translate directly into H100 being six times as fast as Titan X, but, I hear that these GPU workloads might be lengthy... For some of these, you could obviously pay with your time. Sometimes that time is very valuable, and sometimes you have a lot to spare. Also, the amount of VRAM (3x)... Sometimes having too little of it means having to re-write the program, or it could dramatically impact the speed. Similarly for bandwidth (8x). But, again, if the kind of workload you have isn't constrained by either, then you could probably win by running on more smaller / older GPUs. This is a very silly question... the answer is the same as with virtually anything else: real estate, transportation, wedding dresses... I mean, you need to compare the price that will ultimately depend on the kind of thing you are doing... that's all. Sometimes it makes sense to rent, other times it makes sense to buy. There's no one right answer. [1] https://timdettmers.com/2023/01/16/which-gpu-for-deep-learni... I agree with your basic point about building vs renting, but retail GPUs aren't simply more powerful. Retail GPUs are just as fast but have a lot less VRAM and memory bandwidth. The RTX4090 has 24 GB of VRAM with 1 TB/s of bandwidth. The A100 has 80 GB at 2 TB/s. For some tasks, the memory and bandwidth are the strongest constraints. I'm building a GPU accelerated signal processing web-app, and I'm currently planning to deploy it on a $180 Nvidia Jetson.
Performances are a little bit low, but mostly enough to make the app usable. I tried to find GPU instances for < $50/month, but couldn't find any. The only alternative would have been to rent Apple M1 instances at Scaleway, but it's still way more expensive (€80/month) than hosting my 10 watts Jetson at home. Does anybody know cheaper GPU instances? I don't need much computing power, about 0.5Tflops to 1TFlops If you're working with Jetson, the RTX 4000 may be more powerful than you need right now, and it's the least expensive we've got. It's worth exploring if there are cloud service providers that offer lower-end GPUs, as that could potentially be a better fit for your requirements and budget. Whether or not $50/mo is an unreasonable ask (it is), they have a budget and feel that even $85/mo is too far out of budget for their application. Presumably they mean $1.1/hr * 24hr/day * 30day/month = $792/month You are absolutely right, and I apologize if I came across as dismissive. We may have different viewpoints, and it's important to respect and acknowledge each other's perspectives. I appreciate your input, and I'm here to listen and address any further questions or concerns. Let's keep the conversation friendly and open. What's the availability like? I had tried it once and had problem getting any GPU. By the way, Lambda Stack is VERY impressive. Thanks for maintaining that! While they may offer lower prices, the infrastructure may have limitations in terms of reliability, scalability, and security compared to data center-based providers. Additionally, the performance and availability of the services vary depending on the hosts' hardware and internet connection. Our lowest at Genesis Cloud at this time are instances with an RTX 3060Ti for 0.20$/hour which adds up to 146$/month ( https://www.genesiscloud.com/pricing#nvidia3060ti )
Though, this includes free storage, no egress fees and has a lot more power than a Jetson. If you need to optimize for low cost hosting, did you already check whether you actually must have a GPU for your use case? Modern CPU have some impressing capabilities. Maybe it's time to step away from the keyboard for a while? Moreover, the fact that their self-promotion does not align with the intention of the original discussion and GGP explains their purpose. Their primary goal is not genuinely assisting or finding a solution. In such cases, as you can imagine, it's challenging for me to maintain respect. I apologize if my previous comment came across as dismissive. I believe I have expressed my viewpoint clearly, but I'm open to further discussion. Let's continue this conversation in a friendly and respectful manner, but after I come back from my break as you have suggested. :) Azure and AWS pricing for GPU’s is insane. lambdalabs seems to be the cheapest but I haven’t used it. Anyone has experience with it? If you require sustained usage, we offer long-term reservations with discounts of up to 50% off our regular on-demand prices. For assistance in selecting the most suitable option for your specific needs, please feel free to email us at business@oblivus.com. We'll be happy to assist you. Firstly, unlike AWS, GCP, or Azure, there are no restrictions on the number of GPUs you can deploy with us. As long as you have sufficient account balance, you can deploy hundreds of GPUs simultaneously. In terms of availability, we maintain a diverse range of resources from various vendors in our on-demand stock. While we have our own infrastructure, we also leverage infrastructure from other vendors to meet the growing demand. Currently, we have more than 3000 GPUs in stock, with over 2600 of them available for deployment. You can find more detailed information on our availability page at https://oblivus.com/availability/. I hope this helps! > with over 2600 of them available for deployment. guessing you mean 2600 in total. FWIW we ran a workload recently on AWS that required a few thousand g4 instances in a single AWS region. We ended up scavenging and using g3s as well due to capacity constraints. If you're using our on-demand service and intend to terminate the machines once your work is completed, we currently have 2600 available GPUs. However, if you have an ongoing need for these machines, we also have reserved instances with additional stock, which brings our total capacity to an estimated 7000 GPUs as of today. But of course these numbers could easily change in the future. The main difference between our platform and theirs is that we offer fully customizable configurations. From vCPU to RAM, Disk, and GPU, you have the flexibility to customize each aspect according to your specific needs. Unlike pre-set configurations offered by other providers, our platform allows you to tailor your virtual machines to your exact requirements. Also for Windows instance, what do people use to connect. RDP seems slow. My primary use case is software development trying to learn CUDA programing. At the moment, when you shut down a server, you are only billed for the storage and the IP address associated with it. However, we are working on implementing a feature in the near future that will allow you to detach the IP address, enabling you to pay solely for the storage usage. Regarding Windows servers, the default software for remote access is RDP. However, you have the freedom to install and use any other remote access software of your choice on our platform. Hope this clarifies things for you! To clarify, we don't offer any consumer cards (RTX 3080, RTX 4090, etc) mentioned in the thread. Instead, we provide professional cards from the RTX Series such as the RTX 4000 and RTX 5000. "oblivus": 1x RTX A5000 for $0.84/hr
vast: 4x RTX A5000 for $1.08/hr, 1x RTX A5000 for $0.20/hr, and much better and larger selection. "They operate a model known as a "marketplace" or "community" cloud. Unlike us, they utilize servers hosted by individuals in their homes. This is something completely different. While they may offer lower prices, the infrastructure may have limitations in terms of reliability, scalability, and security compared to data center-based providers. Additionally, the performance and availability of the services vary depending on the hosts' hardware and internet connection." Do I get access to a vm with powerful gpus? I would love to be able to execute dockerfiles over the cli that runs gpu intensive executables. To begin, simply create an account at https://console.oblivus.com and add a minimum of $5 to your account balance. Alternatively, you can utilize the promo code HN_1 on the same page for testing purposes. Once you deploy your server, you'll have access to a dedicated virtual machine, providing you with the capability to perform any tasks you would typically carry out on your own computer. You also have the freedom to execute Docker within your virtual machine. Please have a look at our documentation at https://docs.oblivus.com, where we tried to explain everything with images directly from our console. If you have any other questions, I'd be happy to answer them! Our team will look into this matter internally to ensure that everything is functioning properly. We appreciate your feedback! Runpod:
A100 80GB for $1.990/hr vs Oblivus:
A100 80GB for $2.41/hr How do you justify being significantly more expensive? It's important to note that our platform offers full customizability, while they only offer pre-set configurations. This level of flexibility comes with a trade-off in terms of pricing. Regarding their infrastructure and system, I don't have specific information. However, I couldn't find any details about long-term reservations on their website. In comparison, -for example- we offer reserved instances with A100s starting as low as $1.2 per hour. There are also other factors to consider. We prioritize investing in data center infrastructure, security, quality, and reliability. For instance, we provide up to 40Gbps public, 200Gbps private network connectivity for each virtual machine and offer 3x replication for storage. These features come with their own trade-offs, and each company has its own pricing structure. I hope this clarifies the situation! We offer Windows (BYOL) images on our virtual machines. If you have a good latency to our servers and sufficient bandwidth to handle the streaming, you can enjoy cloud gaming using any software of your choice. To get started, you can deploy a CPU-based Windows virtual machine for as low as $0.019/hr. Once you have installed your games, you can stop the virtual machine and modify it into a GPU-based VM for as low as $0.29/hr. While your server is running, you will be billed for all the components. However, if you shut down the server, you will only be billed for the storage and the IP address. Your data will remain safe as long as you don't delete the server, allowing you to start playing again whenever you want. In the coming days, we will be implementing a feature that allows you to detach the IP address, resulting in more cost-effective billing, where you will only be billed for the storage. I hope this information helps! Assuming that: - I game 2 hours a day, - I need 50 GB for Windows and another 200 GB for games, - I need 16 GB of ram and an equivalent of RTX 4080, what would my total cost per day would be? As for finding a GPU equivalent to the RTX 4080, it's challenging as it is a new release and we don't have benchmarks for it yet. However, we can provide pricing estimates for the RTX 5000 and RTX 4000 GPUs. For the RTX 4000, the approximate cost would be around $24, while for the RTX 5000, it would be around $45. Please keep in mind that these are estimates and the actual pricing may vary. Additionally, the optimization we are planning to implement for IP addresses in the near future will further improve the overall pricing. Even though Oblivus doesn't have a specific meaning, we initially didn't make the connection with "oblivious". When we attended Cloudfest recently, many people also pronounced it as "oblivous," so you're not alone in that regard, I suppose. :) We are currently working on developing a feature that will allow users to create snapshots of their system disks and use those images to create multiple instances. We expect this feature to be available to everyone in a few weeks. If you don't mind, could you please send an email to doruk@oblivus.com so that we can discuss this further? PS: I see that your VMs are created in CoreWeave datacenters. I wonder the relationship between your company and CoreWeave. Are you just a reseller? Upon review of their system, it appears that although there may be similarities, there are some notable differences between their service and ours. Specifically, it seems that their CPU-based virtual machines come with preset configurations, lacking full customizability. Additionally, I was unable to locate an additional storage solution, reserved instances, organization and auto top-up system on their platform. On the other hand, we do not plan on offering anything similar to their "marketplace cloud" with individual hosts. Assume I try something out on a GPU server and I realize I made some wrong assumption in my code and I need to think things over, so I shut down the instance after 2h 18min 24s of usage. What do I pay for? Ed. Nevermind, I found the answer here https://docs.oblivus.com/billing/payment-plans, seems to be per minute, so I assume 2h 19min. Yes, we bill our services on a minute basis, so you will only be charged for the actual usage duration, which in this case would be 139 minutes (2 hours and 19 minutes). You can find detailed information about the resources you have used and their respective costs on the Billing > Invoices page. I hope this helps! That sounds like an excellent suggestion. Would you mind sending an email to doruk@oblivus.com with a brief description of the video and details about your channel? Thanks! We have plans to expand to the EU, potentially in the third quarter of 2023. Building our current infrastructure in the EU is quite difficult, especially with the high electricity prices in EU data centers. However, we have been in discussions with various providers to establish an infrastructure that meets our requirements. Additionally, we are actively working on a solution called Oblivus Edge Deployment, which aims to offer low-latency and high-bandwidth connectivity to EU users, even if the server is located in the US. We expect to release this technology in the next few weeks.133 comments
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