Life & fun · September 3, 2026
The cheapest GPU cloud – H100s at $2.04/HR, H200s at $3/HR
Affordable H100, H200, GB300, and B200 GPU compute for training, inference, and everything in between.
The cheapest GPU cloud – H100s at $2.04/HR, H200s at $3/HR launched on September 3, 2026 with 63 votes, #574 of 8,093 launches that month and more than 95% of that year's launches.
In plain words
Compute Cheap provides access to high-end GPU compute resources including H100, H200, GB300, and B200 processors for machine learning training and inference tasks. The service offers H100s starting at $2.04 per hour and H200s at $3 per hour, with options for either interruptible or reserved capacity. Users can compare pricing against market benchmarks, with minimum reservations of 2,000 GPU-hours and a 50% upfront payment required.
written from the facts on this page · September 2026
From the sources
Affordable H100, H200, and B300 interruptible GPU compute for training, inference, and everything in between.
Prepaid interruptible H100, H200, and B300 GPU-hours in the US and EU. Select H100, H200, or B300 interruptible capacity. B200 is sold out. Hover or focus the chart to compare the daily market benchmark with the Compute Cheap price. When the chart is focused, use the left and right arrow keys to inspect dates. Run frontier GPUs for less. Choose H100, H200, or B300. B200 is currently sold out. Choose H100, H200, or B300 interruptible capacity, and how many GPU-hours you need. Minimum reservation is 2,000 GPU-hours. Capacity is limited, so we confirm it first. You prepay 50% upfront at the quoted rate, and your interruptible block is booked. Run your workload at the quoted interruptible rate.…from compute.cheap
Does a similar job
all alternatives →- SFSan Francisco Compute – 512 H100s at <$2/hr for research and startups2023 · sfcompute.org · ▲727
Hey folks! We're Alex and Evan, and we're working on putting together a 512 H100 compute cluster for startups and researchers to train large generative models on. - it runs at the lowest possible margins (<$2.00/hr per H100) - designed for bursty training runs, so you can take say 128 H100s for a week - you don’t need to commit to multiple years of compute or pay for a year upfront Big labs like OpenAI and Deepmind have big clusters that support this kind of bursty allocation for their researchers, but startups so far have had to get very small clusters on very long term contracts, wait…
On-Demand H200 GPU Cloud from $0.79/HR2025 · hpc-ai.com · ▲20Hi HN, We’ve launched a GPU cloud platform offering on-demand access to high-end GPUs — with pricing starting at $0.79/hr, no queue, and no preemption. What we offer: - On-demand H100, H200, and B200 instances - H200 pricing from $0.79/hr - Multi-region support: US, Singapore, and Europe - No queuing, no bidding, and no interruptions - Used by AI startups, researchers, and open-source developers Try it: https://hpc-ai.com/?utm_source=hackernews&utm_medium=post&ut... We built this after facing our own frustrations with high prices and long wait times on other…
- TCTensorDock Core GPU Cloud – GPU servers from $0.29/hr2022 · tensordock.com · ▲147
Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…
Compute Prices2025 · computeprices.com · ▲6Trying to get a better understanding for the economics of compute, started with a tracker. Most importantly, it needs more data. Anything else I should add?
Prime Intellect GPU Cloud. H100s starting at $1.65/HR on demand2024 · app.primeintellect.ai · ▲7Compute, frameworks, and infrastructure for you to train, fine-tune and deploy your own agentic models.
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