[Reference page]() # H100 SXM: 80 GB to expand your PyTorch project. Your project needs more memory on a single card. Rent a NVIDIA H100 SXM 80GB from Kernodeck, with 80 GB per GPU for a Hopper-compatible application. The 3-day plan costs 203.57 USD for 1 lot of 1 GPUs: an option to consider when 48 GB limits your scenario. - 80 GB on one card - For your Hopper-compatible projects - Full price for 3, 7, or 30 days [Configurer the H100 SXM ยท 7 days]() At Kernodeck, no ID and no KYC process on this GPU rental path. Account required: first name, last name, email and password; that does not mean anonymity. Location NVIDIA H100 SXM 80GB Move up to 80 GB per card while keeping your period's cost visible. ## Key facts | Fact | Detail | | --- | --- | | Your configuration | NVIDIA H100 SXM 80GB : 1 batch of 1 GPU, 80 GB per GPU. | | Your plans | 3 days: 203.57 USD ; 7 days: 475.00 USD ; 30 days: 1,690.00 USD. USD price for the entire batch, including all its cards. | | Availability | 63 declared batches. Declared quantities per model in the offers. They do not reserve future availability or a delivery date. Hosting country and served region to be confirmed before purchase if your project requires them. | | No KYC | At Kernodeck, no ID and no KYC process on this GPU rental path. Account required: first name, last name, email and password; that does not mean anonymity. | | Setup | Ubuntu, PyTorch, Blender or a custom request. Versions, CPU, RAM, storage, network and access mode to be confirmed per project; their inclusion is not implied by the GPU model. | | Pricing and terms | USD amounts for the lot and the entire period. Tax status and any fees to be confirmed in the applicable terms. | ## Available plans Total price in USD for one lot and the full period. Memory is shown per GPU; declared stock is expressed in lots. | Model | Memory per GPU | GPUs per lot | Duration | Lots | Total price | Declared stock in lots | Status | Configuration | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | [NVIDIA H100 SXM 80GB]() | 80 Go | 1 | 3 days | 1 | 203.57 USD | 63 | The full batch for 3 jours. | [Configure this plan]() | | [NVIDIA H100 SXM 80GB]() | 80 Go | 1 | 7 days | 1 | 475.00 USD | 63 | The full batch for 7 jours. | [Configure this plan]() | | [NVIDIA H100 SXM 80GB]() | 80 Go | 1 | 30 days | 1 | 1,690.00 USD | 63 | The full batch for 30 jours. | [Configure this plan]() | | [NVIDIA H100 PCIe 80GB]() | 80 Go | 1 | 7 days | 1 | 420.00 USD | 27 | Compare the 7-day budget and the memory per card. | [Configure this plan]() | | [NVIDIA H200 SXM 141GB]() | 141 Go | 1 | 7 days | 1 | 640.00 USD | 19 | Compare the 7-day budget and the memory per card. | [Configure this plan]() | ## A candidate when memory becomes your purchase criterion. A PyTorch scenario that overflows a 48 GB card is worth comparing against the H100 SXM. For fine-tuning, count the activations and training states in addition to the weights; for inference, go by the allocations of your actual run. The choice becomes relevant if that need fits within 80 GB and your dependencies are Hopper-compatible. For a series of configurations with evaluation and checkpoints, budget 475.00 USD for 1 lot of 1 H100 SXM over 7 days. If your priority is deciding whether the scenario suits this card, the 3-day plan is 203.57 USD. The period funds your work on the application, without guaranteeing a number of experiments or their outcome. - [Configure my H100 SXM for 7 days]() - [Size my inference application]() - [Prepare to resume my campaign]() ## Same capacity or more room: compare the right criterion. The H100 PCIe also has 80 GB per GPU and a lower plan in the Kernodeck catalog. For mostly single-card work, that's a useful comparison. The SXM name alone isn't enough to conclude there's an advantage for your application: the topology and the exchanges actually used remain decisive if you work with multiple cards. The H200 SXM offers 141 GB per GPU. Consider it when the identified problem is the card's capacity, rather than ordering a second H100 on the assumption that the two memories will pool together. You're then comparing a higher memory need against its rental budget. - [Compare the H100 PCIe 80 GB plan]() - [Consider 141 GB with the H200 SXM]() - [Compare Hopper plans]() ## Your plan, payable directly in crypto. Pay for your Kernodeck rental directly in crypto, with no mandatory top-up: BTC on Bitcoin and USDT on Tron (TRC-20), among others. The process requires neither an ID document nor a KYC procedure; an account with first name, last name, email and password is required, with no promise of anonymity. - [Compare the eight accepted assets and networks]() ## Start with a complete training step Your first test should run through data loading, the forward pass, gradient computation, and the optimizer update. Measure this full cycle before increasing the batch. Weights are only part of the memory used: activations and training states must also fit within the 80 GB. Keep a representative sequence of inputs, including the large examples. You'll avoid sizing the entire campaign based on a particularly easy first batch. ## Mixed precision and controlled comparison PyTorch lets you choose a precision suited to certain operations with autocast. Test this setting on your loss function and validation criteria. Enabling an option is not proof of numerical stability: keep a comparison run, metrics, and the exact parameters. For multiple cards, distinguish per-GPU batch from global batch. The number of processes and gradient accumulation are part of the protocol, just like the learning rate. ## SXM, PCIe, or more memory The H100 PCIe keeps an 80 GB capacity and is worth comparing for primarily single-card work. For frequent GPU-to-GPU exchanges, measure the topology actually used by your application; the SXM name does not replace this check. Prefer the H200 if the identified problem is primarily a lack of memory per card. ## Reserve a period that includes recovery Choose 3 days to validate a loop and a checkpoint, 7 days for an ablation series, or 30 days for extended experiments. Include evaluation and export in that schedule, not just training epochs. In the configurator, select the PyTorch or custom setup, your batches, and your duration. Create your account or log in before saving the order and choosing crypto payment. The "I've paid" button is used to report the transfer; payment tracking remains visible in your account. ## Questions before purchase ### How much does a lot of H100 SXM cost, and what does that unit include? One lot corresponds to 1 H100 SXM of 80 GB. The full plans are 203.57 USD for 3 days, 475.00 USD for 7 days, and 1,690.00 USD for 30 days. The quantity chosen in the configurator corresponds to the number of lots; each additional lot adds its card and its price. ### When do the H100 SXM's 80 GB justify this choice? When your scenario exceeds a lower capacity and can fit within 80 GB, with a Hopper-compatible stack. For training, include activations, gradients, and optimizer states. For inference, take into account the application's allocations beyond the weights alone. ### Why compare the H100 PCIe before choosing SXM? The H100 PCIe keeps 80 GB per GPU with a lower plan in the Kernodeck catalog. So it's worth considering for a single-card project. A need for inter-card exchanges must be evaluated with the actual topology; the SXM label alone doesn't prove an application-level gain. ### Should you take two H100s or move up to the H200 if 80 GB isn't enough? If your application expects more memory on a single card, first compare the H200 SXM's 141 GB. Two H100s remain two 80 GB memories: using them together requires suitable software and a distribution plan. The number of cards doesn't replace that application architecture choice. ## Choose and configure - [Build a fine-tuning protocol]() - [Test training resumption]() - [Compare H100 SXM and PCIe]() - [Move to 141 GB per card]() - [Compare GPU plans]() - [The eight accepted crypto pairs]() - [Choose a capacity for inference]()