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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesShort answer: the published North American price for an eight-GPU CoreWeave HGX H100 node is $49.24 per hour on demand or $19.71 per hour on spot. That is $6.16 or $2.46 per GPU-hour, respectively. A 100-hour run on the full node would therefore cost $4,924 on demand or $1,971 on spot before storage, data transfer, CPU, orchestration and other charges. CoreWeave also lists eight-GPU L40, A100 and H200 nodes at different rates, while a checked September 2026 comparison puts AWS below Google Cloud and Azure for comparable eight-H100 on-demand instances. The cheapest quoted GPU is not automatically the cheapest training run: utilization, interruptions, checkpointing and the rest of the hardware bundle determine the final bill.
CoreWeave GPU prices at a glance
CoreWeave’s 2026 North American list prices below are instance-hour prices for complete eight-GPU nodes. The per-GPU figures are arithmetic conversions, not separate products or billing units.
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| Configuration | On-demand, per node-hour | Spot, per node-hour | On-demand, per GPU-hour | Spot, per GPU-hour |
|---|---|---|---|---|
| Eight-GPU L40 | $10.00 | $6.27 | $1.25 | $0.78 |
| Eight-GPU A100 | $21.60 | $9.65 | $2.70 | $1.21 |
| Eight-GPU H100 | $49.24 | $19.71 | $6.16 | $2.46 |
| Eight-GPU H200 | $50.44 | $20.93 | $6.31 | $2.62 |
For example, renting one eight-H100 node for 100 wall-clock hours is $4,924 on demand or $1,971 on spot. The same period costs $5,044 or $2,093 for an eight-H200 node, $2,160 or $965 for an eight-A100 node, and $1,000 or $627 for an eight-L40 node. These are compute-only calculations using the listed rates; they do not imply that each GPU type delivers the same training speed, memory capacity or network performance.
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— CoreWeave pricing documentation
That warning is important because a node price is only one line in an AI budget.
What an hourly GPU quote actually covers
Instance pricing versus GPU pricing
Cloud providers normally quote the complete virtual machine or bare-metal instance. An eight-GPU rate includes eight accelerators and a particular bundle of host CPUs, system memory, networking and local or attached storage. Divide by eight only when comparing the GPU component with another eight-GPU configuration. Comparing a $49.24 node directly with a $6.16 single-GPU price can lead to an eightfold error.
CoreWeave Classic is a different billing model
CoreWeave Classic uses a la carte pricing. Its total combines the GPU component with the vCPUs and RAM you request, plus storage and any other metered resources. A valid virtual-server configuration requires at least one GPU, one vCPU, 2 GB of RAM and at least 40 GB of root-disk NVMe storage. The listed H100 PCIe GPU component is $4.25 per hour before those additional resources. That single-GPU component is not the same product as the eight-GPU HGX H100 node priced at $49.24 per hour, so use the Classic figure only after adding the machine resources your workload needs.
Forge Serverless charges active training time
CoreWeave’s Forge documentation gives a separate post-training model: Serverless supervised fine-tuning (SFT) and reinforcement learning (RL) cost $2.70 per hour of active training, prorated. Thirty minutes is $1.35, and idle time is not charged. Inference and checkpoint storage are billed separately. This can be attractive for short or bursty jobs, but it should not be compared directly with an always-on eight-GPU node unless the amount of parallel hardware and the elapsed training time are equivalent.
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How to calculate the all-in cost of a training run
Use this model before choosing a provider:
Estimated bill = (GPU instance hours × instance rate × number of nodes) + CPU/RAM charges + storage + network transfer + orchestration or support + inference and evaluation services
Then adjust the GPU-hours for the actual wall-clock behavior of the job:
- Measure the hardware requirement. Record GPU type, GPUs per node and number of nodes. A distributed run may need whole nodes even when the model does not keep every GPU busy.
- Estimate elapsed hours. Base this on a representative throughput test, including data loading, validation and checkpoint pauses, rather than multiplying a theoretical token rate.
- Choose the billing mode. Apply the on-demand, spot or committed rate that is actually available in the target region.
- Add separately metered resources. Include vCPUs, RAM, root and scratch disks, persistent datasets, checkpoint retention and object-storage operations.
- Price data movement. Account for ingress and egress, cross-region transfers, traffic between nodes and any storage-to-compute transfer charges.
- Model failure and recovery. Add expected requeued work, restart time, repeated data preparation and extra checkpoints when using interruptible capacity.
- Price the complete workflow. Evaluation, batch inference, serving endpoints, experiment tracking and orchestration may continue after the training process stops.
Worked examples using the published rates
One eight-H100 node
| Elapsed time | On-demand at $49.24/hour | Spot at $19.71/hour |
|---|---|---|
| 10 hours | $492.40 | $197.10 |
| 100 hours | $4,924.00 | $1,971.00 |
| 500 hours | $24,620.00 | $9,855.00 |
These totals assume one node runs continuously for the stated period. If a spot interruption causes 20 hours of lost work and the job has to repeat that work, the effective GPU bill is based on 120 hours, not 100, before counting the engineering time and extra storage.
Comparing the other CoreWeave node types
| Node | 100 hours on demand | 100 hours on spot |
|---|---|---|
| Eight L40 | $1,000 | $627 |
| Eight A100 | $2,160 | $965 |
| Eight H100 | $4,924 | $1,971 |
| Eight H200 | $5,044 | $2,093 |
The table shows price, not time-to-result. A cheaper accelerator can become more expensive if it takes substantially longer, cannot hold the model or requires a different parallelization strategy.
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A Forge Serverless fine-tuning job
At $2.70 per active hour, 10 active hours of SFT or RL is $27.00 and 100 active hours is $270.00. A 30-minute active run is $1.35. Paused or idle time is excluded from that training charge, but inference and checkpoint storage remain separate line items.
CoreWeave versus AWS, Google Cloud and Azure
A September 2026 comparison checked eight-GPU H100 configurations from the major providers. The listed hourly rates were:
| Provider and instance | Instance price per hour | Derived price per GPU-hour | 100-hour instance total |
|---|---|---|---|
| CoreWeave eight-GPU HGX H100, on demand | $49.24 | $6.16 | $4,924 |
| AWS p5.48xlarge | $55.04 | $6.88 | $5,504 |
| Google Cloud a3-highgpu-8g | $88.49 | $11.06 | $8,849 |
| Azure ND96isr H100 v5 | $98.32 | $12.29 | $9,832 |
On this checked list, CoreWeave has the lowest on-demand instance price, followed by AWS, Google Cloud and Azure. The comparison explicitly cautions that the instances are not identical: CPU, system memory, storage and networking differ, and rates change by region and time. A fair procurement decision therefore compares the complete bundle and measured training throughput, not just the H100 label.
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CPU, RAM and local storage
Data preprocessing, tokenization, checkpoint compression and evaluation can require substantial CPU and memory. Under an a la carte model, under-sizing these resources may leave GPUs waiting; over-sizing them raises the bill even when GPU utilization is low. Root disks, local NVMe scratch space and persistent volumes can have different rates and retention rules.
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Checkpoint and dataset storage
Frequent checkpoints reduce lost work after an interruption but increase storage capacity, write operations and retention costs. Keep only the checkpoints needed for rollback, validation and final export, and distinguish temporary scratch data from durable copies.
Network transfer
Large datasets, cross-region replication, multi-node collectives and exporting a finished model can all generate traffic charges. Place compute near the dataset when possible and include egress in the estimate if the model or results must leave the provider.
Evaluation, inference and orchestration
Training is often followed by benchmark suites, preference evaluation, batch generation or an always-on endpoint. Those services can continue billing after the training node is deleted. Managed schedulers, support plans and experiment-tracking services may also be metered separately.
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On-demand, spot and reserved capacity: the real trade-off
On-demand
On-demand capacity is the simplest baseline for a deadline-sensitive run. It avoids a long commitment and generally offers predictable availability, but it carries the highest hourly price in the figures above.
Spot or interruptible capacity
CoreWeave’s listed eight-H100 spot price is about 60% below its on-demand rate. The saving is meaningful only if the training stack can tolerate eviction. Use resumable checkpoints, automatic job requeueing, idempotent data preparation and a bounded checkpoint interval. Track the effective cost per completed training hour, including discarded work and idle time while waiting for replacement capacity.
Reservations and commitments
Longer commitments can reduce a quoted rate, but they exchange flexibility for a utilization obligation. Reserve only the baseline capacity you expect to use; leave burst capacity on on-demand or spot when the provider and workload support it. Verify cancellation terms, region restrictions and whether the commitment covers the exact GPU generation.
A practical budgeting workflow
- Define the completion target. State the model version, dataset, number of passes or training steps and evaluation gates.
- Run a short pilot. Measure tokens or samples per second, GPU utilization, host-memory use and checkpoint duration on the intended instance.
- Convert the pilot to wall-clock time. Include validation, data loading, planned checkpoints and a recovery allowance.
- Price at least two capacity modes. Show an on-demand ceiling and a spot estimate with explicit interruption assumptions.
- Add non-GPU resources. Ask the provider or read the configuration calculator for CPU, RAM, disks, object storage, transfer and support charges.
- Set a spending guardrail. Configure budget alerts, maximum runtime and automatic shutdown for failed or idle jobs.
- Reconcile after completion. Compare billed GPU-hours with productive GPU-hours; use the difference to improve the next estimate.
Common mistakes and how to avoid them
- Dividing the wrong number: divide a complete eight-GPU node rate by eight only for a per-GPU comparison; do not treat that result as a single-GPU quote.
- Mixing hardware bundles: an H100 instance with more CPU, RAM or faster networking can cost more while finishing a job sooner.
- Ignoring idle periods: queue delays, data staging and paused workers can consume paid instance time, even when no training step is executing.
- Assuming spot is free of risk: interruptions create restart, checkpoint and scheduling costs. Test recovery before moving a long run to spot.
- Leaving services behind: delete stopped instances, unattached volumes, snapshots, inference endpoints and temporary buckets when the experiment ends.
- Using a one-region quote globally: the published comparison warns that rates vary by region and time. Recheck the target region immediately before committing.
Which cloud is cheapest for LLM training?
There is no universal winner. For the checked eight-H100 on-demand configurations, CoreWeave has the lowest listed instance price and AWS is next; Google Cloud and Azure are higher in that comparison. CoreWeave’s spot rate is lower still, but only a fault-tolerant workload can realize that theoretical saving. A team with existing data, identity, monitoring and deployment systems in another cloud may spend less overall there by avoiding migration and transfer costs.
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Quick Recap
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