For occasional use, a pay-per-token API with your own key is usually cheapest; for heavy steady use, a rented GPU serving it yourself wins; running locally is only 'free' if you already own a 48GB-class rig or a big Mac.
A 70B model is right at the boundary where the cheapest way to run it depends heavily on how much you'll use it — there's no single answer, only a rule that maps your usage to the best route. A 70B at the common 4-bit quant needs roughly 40 GB of VRAM for weights plus several GB of headroom, which means it does not fit a single 24 GB consumer card. That hardware reality shapes every option.
Option one, run it locally, is genuinely $0 in compute — but only if you already own hardware that can hold it: two 24 GB GPUs together, a 48 GB workstation card, or an Apple Silicon Mac with 48 GB or more of unified memory. If you already have such a rig, local is unbeatable: after electricity, running the 70B costs nothing no matter how much you use it, and everything stays private and offline. But if you'd have to buy that hardware just to run a 70B, the up-front cost is substantial, and it only pays off if your usage is heavy and sustained enough to justify it over simply renting or calling an API. So local is the cheapest route for people who already own the rig or who will run a 70B constantly for a long time.
Option two, a pay-per-token API with your own key, is usually the cheapest for occasional or light use. You pay the provider's real per-token rate directly (no reseller markup), nothing when idle, and you reach a hosted 70B instantly without owning any hardware. For someone who wants to use a 70B now and then — a few sessions a week, some batch jobs, experimentation — this almost always beats buying or renting a GPU, because you're paying only for the tokens you actually consume. The downside is that cost scales with usage, so heavy sustained use eventually makes the metered bill add up, and your data goes to the provider.
Option three, renting a cloud GPU by the hour and serving the 70B yourself with a high-throughput engine like vLLM, wins for heavy, steady, predictable volume. An 80 GB H100 or A100 holds a 70B comfortably, and if you keep that GPU busy — many concurrent requests, a big batch job — the flat hourly rate spread across a huge number of tokens can drop the cost per token well below any API. But a rented GPU sitting mostly idle is expensive: you pay for every hour whether or not you use it. So renting is cheapest specifically when your utilization is high; for light use it's the most expensive of the three.
The honest decision, roughly: use an API with your own key if your 70B usage is light, occasional, or unpredictable; run it locally if you already own 48 GB-class hardware or a big Mac (or your usage is heavy enough long-term to justify buying one); rent a GPU and self-serve with vLLM if your volume is high and steady enough that a flat hourly rate beats per-token pricing. The crossover between the API and rented-GPU routes is a real break-even set by your token volume and how busy you'd keep the GPU.
A reframe worth considering before spending anything: do you actually need a 70B? Parameter count drives cost and memory, not quality directly, and a strong 30B-class model at 4-bit fits comfortably on a single 24 GB card that you might already own — making it effectively free to run locally. For many tasks the gap to 70B isn't worth the extra hardware or per-token cost, so it's genuinely worth trying a 32B-class model that fits your existing card first. If it's good enough, that's the cheapest way of all.
Spanvero prices all three routes for every 70B-class model with zero markup, so you can find the real cheapest option for your own volume. Enter your expected usage at /calculator/ to compare local, rented-GPU, and your-own-key API costs; browse 70B-capable setups at /models/48gb-vram/; check whether a smaller model that fits a single card would do at /models/24gb-vram/; and see the full memory math in the guide at /learn/vram-for-70b/.
How much VRAM does a 70B model need? · Local vs API vs renting a GPU · Is renting an H100 worth it? · How much does it cost to run an AI model? · H100 vs A100 for inference · Quantization · vLLM · How do I choose which AI model to run?
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