The H100 is the newer, faster card with more memory bandwidth; the A100 is older, cheaper to rent, and often plenty for many inference jobs — the right choice is the cheapest one that has enough VRAM and throughput for your specific workload.
The H100 and A100 are NVIDIA's two most common data-center GPUs for AI, and if you're renting a cloud GPU to serve or fine-tune a model, choosing between them is a real cost decision. The honest framing is that neither is universally "better" — the right pick is the cheapest card that has enough memory and throughput for your specific job.
Start with what each offers. The A100 is the older generation, available in 40 GB and 80 GB memory variants, and it remains a very capable inference card. The H100 is the newer generation: faster, with substantially higher memory bandwidth and compute, typically around 80 GB of memory, and hardware features (like support for lower-precision formats) that speed up modern inference. In raw performance the H100 is the stronger card. But it also rents for more per hour, and that price gap is the crux of the decision.
For inference specifically, the two questions that decide it are: does the card have enough VRAM to hold your model (plus its KV cache at your context and concurrency), and does it have enough throughput to serve your traffic at an acceptable speed and volume? If an 80 GB A100 comfortably fits your model and serves your load fast enough, it's often the more economical choice — you're not paying the H100 premium for headroom you don't use. If your model or context is memory-hungry enough to need the H100's bandwidth, or your traffic is high enough that the H100's greater throughput lets you serve it on fewer GPUs (or one instead of two), the H100 can be cheaper overall despite its higher hourly rate, because it does more work per hour.
That last point is the subtle one: the higher-throughput card isn't automatically more expensive per token. If an H100 serves twice the tokens per hour of an A100 at, say, 1.5x the hourly price, the H100 is cheaper per token for a fully-utilized, throughput-bound workload. Conversely, for a lightly-loaded service where neither card is near capacity, the cheaper A100 wins because you're paying mostly for idle time either way and might as well pay less for it. So the comparison depends on how busy you'll keep the card and whether your workload is bound by memory, bandwidth, or is simply light.
VRAM capacity can also be the deciding factor on its own. A 40 GB A100 can't hold a 70B model at 4-bit (which needs ~40 GB plus KV-cache headroom), while an 80 GB A100 or H100 can. If your model needs 80 GB, you're choosing between the 80 GB A100 and the H100 on price and speed; if it fits in 40 GB, the cheaper 40 GB A100 may be all you need. Match the memory to the model first, then optimize on throughput and price.
The honest guidance: don't default to the H100 because it's the flagship. For many inference workloads — moderate models, moderate traffic — an A100 (especially the 80 GB variant) is plenty and cheaper to rent. Reach for the H100 when you genuinely need its bandwidth or throughput: very large models, long contexts, high concurrency, or when consolidating onto fewer GPUs saves money overall. And remember the bigger picture from the renting-a-GPU decision: a rented GPU of either type is only cheap per token if you keep it well-utilized — for light or spiky use, a pay-per-token API with your own key usually beats renting either card.
Spanvero prices rented-GPU routes at the vendor's real hourly rates with zero markup, so you can compare cards for your actual model and volume instead of assuming the newest is best. Use /calculator/ to enter your workload and see the honest cost across GPU options, local, and your-own-key API; compare the cards on the per-GPU pages under /gpu/; and read when renting a top-tier card is worth it at all in the guide at /learn/is-renting-an-h100-worth-it/.
Is renting an H100 worth it? · Local vs API vs renting a GPU · vLLM · What's the cheapest way to run a 70B model? · How much VRAM does a 70B model need? · What GPU should I buy for running local LLMs? · How much does it cost to run an AI model? · Inference
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