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For the North American prices checked in 2026, an eight-GPU CoreWeave node costs $49.24 per hour on demand or $19.71 per hour on spot when the GPUs are H100s. The same price list shows eight-GPU L40, A100 and H200 nodes at $10, $21.60 and $50.44 per hour on demand. In a September 2026 comparison of eight-H100 instances, AWS was $55.04 per hour, Google Cloud $88.49 and Azure $98.32. Those are instance prices, not complete training bills: CPU, RAM, storage, networking, checkpoints, orchestration, evaluation and recovery can materially change the total.
CoreWeave GPU prices at a glance
Cloud providers normally quote a whole virtual machine or bare-metal node by the hour. Because each CoreWeave figure below represents eight GPUs, dividing by eight gives a useful per-GPU-hour estimate, but it does not turn the configurations into identical machines.
| CoreWeave node | On-demand node-hour | Spot node-hour | Derived on-demand GPU-hour | Derived spot GPU-hour |
|---|---|---|---|---|
| Eight L40 GPUs | $10.00 | $6.27 | $1.25 | $0.78 |
| Eight A100 GPUs | $21.60 | $9.65 | $2.70 | $1.21 |
| Eight H100 GPUs | $49.24 | $19.71 | $6.16 | $2.46 |
| Eight H200 GPUs | $50.44 | $20.93 | $6.31 | $2.62 |
The figures are North American list prices shown for 2026. The per-GPU columns are arithmetic derived from the node prices and are rounded to the nearest cent; CoreWeave bills the instance configuration, not necessarily an independently rentable single GPU. Region, availability, commitment term and pricing updates can change what you actually pay.
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The basic estimate is:
Total estimate = GPU nodes × wall-clock hours × node rate + separately metered CPU and RAM + storage + data transfer + orchestration or support + inference and evaluation services.
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Multiply the compute term by the number of nodes, not merely by the number of GPUs you hope to use. Then account for utilization: a job that keeps eight H100s busy for 20 hours but spends four hours compiling, waiting for input, or restarting still occupies the node for the billed period.
GPU instance time
On-demand capacity is the straightforward option: you pay the listed hourly rate while the instance exists. Spot capacity is cheaper but can be reclaimed, so the effective cost depends on checkpointing, restart time and whether capacity is available when the job needs to resume.
CPU, RAM and local disks
Some services bundle these resources into a machine price; others meter them separately. A training job may need substantial host memory for data loading, preprocessing and checkpoint conversion. Temporary local NVMe space can also be essential even when the durable checkpoint is stored elsewhere.
Durable storage and checkpoints
Model weights, optimizer states, datasets and frequent checkpoints continue to consume storage after a run stops. Checkpoint frequency is a financial decision as well as a reliability decision: more frequent checkpoints reduce lost work after interruption but increase storage writes and retained capacity.
Network transfer
Moving a multi-terabyte dataset into the cloud, synchronizing shards between regions, or exporting checkpoints can add transfer charges. A nominally inexpensive GPU is less attractive if the training pipeline repeatedly crosses regions or providers.
Non-training services
Inference used for evaluation, reward-model scoring, data generation or post-training is often billed independently. Monitoring, managed orchestration and support can be additional line items. Include them in the estimate rather than treating the GPU rate as an all-in price.
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Worked CoreWeave cost examples
One day on eight H100 GPUs
A 24-hour uninterrupted run on one eight-H100 node costs $1,181.76 at the $49.24 on-demand rate (49.24 × 24). The same nominal runtime at the $19.71 spot rate is $473.04. These totals exclude every separately metered resource and assume no interruption or retry.
One week on eight H100 GPUs
Seven days is 168 hours. On demand, 49.24 × 168 equals $8,272.32; spot is 19.71 × 168, or $3,311.28. If a pre-emption causes 30 hours of lost work and a restart, the lower spot rate may no longer produce the lower completed-run cost.
Three eight-H100 nodes for 72 hours
A distributed job using three nodes for 72 hours consumes 216 node-hours. At the on-demand price that is $10,635.84 (49.24 × 216); spot compute alone is $4,257.36 (19.71 × 216). Add the cluster’s CPU, RAM, storage, network and service charges, plus any failed or repeated attempts.
CoreWeave’s two different pricing models
Classic virtual-server pricing
CoreWeave Classic describes pricing as a la carte. A valid virtual-server configuration needs at least one GPU, one vCPU, 2 GB of RAM and at least 40 GB of root-disk NVMe storage. Its H100 PCIe GPU component is listed at $4.25 per hour before the requested vCPUs, RAM and disk are added.
That $4.25 figure is a GPU component, not the price of the eight-GPU HGX H100 node quoted above. Comparing the two without adding the Classic server resources would understate the Classic total, while comparing an eight-GPU node with a single PCIe component would overstate its equivalence.
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CoreWeave Forge documentation gives a separate example for Serverless supervised fine-tuning (SFT) and reinforcement learning (RL): $2.70 per active training hour, prorated by usage. Thirty minutes therefore costs $1.35. Idle time is not charged under that example, while inference and checkpoint storage are billed separately.
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Forge’s active-hour model can be useful for bursty post-training that would otherwise leave a provisioned machine idle. It should not be substituted for the eight-GPU node rates when estimating a long-running, continuously allocated pre-training job.
CoreWeave versus AWS, Google Cloud and Azure
The following comparison was checked in September 2026 for listed eight-H100 on-demand configurations:
| Provider and instance | Node price per hour | Derived price per GPU-hour |
|---|---|---|
| CoreWeave, eight-H100 node | $49.24 | $6.16 |
| AWS p5.48xlarge | $55.04 | $6.88 |
| Google Cloud a3-highgpu-8g | $88.49 | $11.06 |
| Azure ND96isr H100 v5 | $98.32 | $12.29 |
On the listed rates, CoreWeave has the lowest on-demand node price and AWS is cheaper than Google Cloud and Azure. The instances are not identical bundles: CPU count, system memory, local storage, networking, region and scheduling terms differ. A lower hourly number is not proof of a lower cost per trained token or per successful run.
How to compare clouds on an equal basis
Match the accelerator configuration
Confirm GPU model, memory capacity, precision support and the number of GPUs per node. An H200’s memory profile may allow a larger batch or model partition than an H100, reducing the number of nodes or hours required even when its hourly rate is slightly higher.
Check the interconnect and topology
Large-model training is sensitive to GPU-to-GPU bandwidth and latency. Compare the node’s interconnect, whether all GPUs share the expected fabric, and the network available between nodes. A cheaper node that scales poorly can cost more for the same completed run.
Normalize host resources
Record vCPUs, system RAM, local NVMe, attached block storage and network bandwidth. If one provider’s quote includes these and another meters them separately, add the missing components before ranking prices.
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Use the same region and data path
Regional rates and capacity vary. Include the cost and time to place the dataset, checkpoints and evaluation services near the GPUs. Egress charges can dominate a workflow that exports every checkpoint or serves frequent evaluation requests outside the provider.
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Measure useful throughput
For a fair experiment, record tokens per second, samples per second, scaling efficiency and hours lost to queueing or failures. Divide the complete bill by successful training work, not by the hours an instance was nominally reserved.
On-demand, spot and committed capacity
On-demand
Choose on-demand when a deadline, synchronized multi-node launch or limited checkpoint tolerance makes interruption expensive. It is also the cleanest baseline for a first cost estimate.
Spot
Spot can reduce the H100 node rate from $49.24 to $19.71 in the CoreWeave figures, but the discount exchanges price certainty for interruption and availability risk. Use automatic checkpointing, resumable data loaders and a retry policy. Budget extra wall-clock time for queueing and restarts.
Reservations or other commitments
Committed terms can lower the unit price, but they introduce an obligation to consume capacity for a stated period. Compare the committed bill with a realistic utilization forecast, including experimentation downtime, before signing. A discount on unused hours is not a saving.
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A practical budgeting procedure
- Define the run. Write down model size, dataset passes, target sequence length, batch size, precision, expected tokens or samples per second and the number of GPUs required.
- Convert throughput into wall-clock time. Add data-loading, validation, checkpoint and evaluation intervals. Do not use ideal accelerator throughput as the schedule.
- Select a node shape. Record GPU count, memory, CPU, RAM, local disk and interconnect. Decide whether the job can run on one node or requires multi-node networking.
- Price three cases. Calculate on-demand, a realistic spot plan with restart overhead, and any committed option. Keep GPU and non-GPU line items separate.
- Add persistent costs. Estimate dataset storage, retained checkpoints, snapshots, logs, monitoring and expected network transfer for the full project, not just the training window.
- Reserve a failure allowance. Include time for failed launches, software bugs, interrupted spot instances, hyperparameter trials and reruns. State the allowance explicitly so it is not mistaken for measured performance.
- Track actuals. After each run, record billed node-hours, utilization, throughput, interruption minutes, storage growth and egress. Replace assumptions with those measurements for the next estimate.
Ways to lower the bill without compromising the experiment
- Use smaller or fewer GPUs for data preparation, debugging and unit tests; reserve the full cluster for validated training.
- Prototype with short runs and aggressive evaluation gates before committing to a multi-day schedule.
- Checkpoint at an interval that limits acceptable lost work without producing unnecessary retained copies.
- Keep datasets, checkpoints and evaluation workers in the same region when practical.
- Delete temporary disks, failed-run artifacts and obsolete checkpoints on a defined retention schedule.
- Use spot for restart-safe phases and on-demand for fragile final runs or deadline-bound launches.
- Profile input pipelines and communication. If GPUs wait on storage or synchronization, fixing utilization can save more than negotiating a small hourly discount.
Common mistakes and how to correct them
Dividing by eight and calling it the bill
The per-GPU figure is only a comparison aid. You still rent the complete node and its accompanying resources, so multiply the node rate by node-hours first.
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Mixing unlike H100 instances
An eight-H100 machine with different CPUs, memory, storage or network fabric can produce a different training time. Record the complete specification beside every rate.
Ignoring idle and recovery time
Compilers, data staging, validation and restart periods can occupy billed capacity. Include them in wall-clock hours and test resume behavior before relying on spot.
Using a component price as a complete server quote
The $4.25 H100 PCIe Classic figure excludes vCPUs, RAM and root-disk storage. Treat it as one line in an a-la-carte configuration.
Leaving post-training out of a training budget
Inference for evaluation, reward scoring, synthetic-data generation and checkpoint storage can continue after the main training job. Forecast those services through the project’s end date.
Which cloud is cheapest for LLM training?
There is no universal winner. For the specifically listed eight-H100 on-demand configurations checked in September 2026, AWS is cheaper than Google Cloud and Azure, while CoreWeave’s published eight-H100 rate is lower than all three. CoreWeave spot is lower still, at the cost of interruption risk. The cheapest completed training run depends on throughput, availability, utilization, data movement, storage and recovery—not the GPU sticker price alone.
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