Interesting concept. One thing I’m curious about if I’m in a cohort for something like DeepSeek V3 and another user spins up a heavy 24/7 job, how do you keep TTFT from degrading? vLLM’s continuous batching helps, but there’s still a physical limit with shared VRAM/compute. I’ve been grappling with this exact 'noisy neighbor' issue while building Runfra. We actually ended up moving toward a credit per task model on idle GPUs specifically to avoid that resource contention entirely.

Curious how you’re thinking about isolation here. Is there any hard guarantee on a 'slice' of the GPU, or is it mostly just handled by the vLLM scheduler?

> How does billing work?

> When you join a cohort, your card is saved but not charged until the cohort fills. Stripe holds your card information — we never store it. Once the cohort fills, you are charged and receive an API key for the duration of the cohort.

Have any cohorts filled yet?

I’m interested in joining one, but only if it’s reasonable to assume that the cohort will be full within the next 7 days or so. (Especially because in a little over a week I’m attending an LLM-centered hackathon where we can either use AWS LLM credits provided by the organizer, or we can use providers of our own choosing, and I’d rather use either yours or my own hardware running vLLM than the LLM offerings and APIs from AWS.)

I’d be pretty annoyed if I join a cohort and then it takes like 3 months before the cohort has filled and I can begin to use it. By then I will probably have forgotten all about it and not have time to make use of the API key I am paying you for.

This is an excellent idea, but I worry about fairness during resource contention. I don't often need queries, but when I do it's often big and long. I wouldn't want to eat up the whole system when other users need it, but I also would want to have the cluster when I need it. How do you address a case like this?

How is the time sharing handled? I assume if I submit a unit of work it will load to VRAM and then run (sharing time? how many work units can run in parallel?)

How large is a full context window in MiB and how long does it take to load the buffer? I.e. how many seconds should I expect my worst case wait time to take until I get my first token?

This is a great idea! I saw a similar (inverse) idea the other day for pooling compute (https://github.com/michaelneale/mesh-llm). What are you doing for compute in the backend? Are you locked into a cohort from month to month?

$40/mo for deepseek r1 seems steep compared to a pro sub on open ai /claude unless you run 24x7. im not sure how sharing is making this affirdable.

Interesting there's a trickle of low intensity job one can always get running but like glm own plan is $30/mo and something about 300tps now I know that one is subsidized but still.

Do you own the GPUs or are you multiplexing on a 3rd party GPU cloud?

1. Is the given tok/s estimate for the total node throughput, or is it what you can realistically expect to get? Or is it the worst case scenario throughput if everyone starts to use it simultaneously?

2. What if I try to hog all resources of a node by running some large data processing and making multiple queries in parallel? What if I try to resell the access by charging per token?

Edit: sorry if this comment sounds overly critical. I think that pooling money with other developers to collectively rent a server for LLM inference is a really cool idea. I also thought about it, but haven't found a satisfactory answer to my question number 2, so I decided that it is infeasible in practice.

Interesting direction. One adjacent pattern we've been working on is a bit less about partitioning a shared node for more tokens, and more about letting developers keep a local workflow while attaching to an existing remote GPU via a share link / CLI / VS Code path. In labs and small teams we've found the pain is often not just allocation, but getting access into the everyday workflow without moving code + environment into a full remote VM flow. Curious whether your users mostly want higher GPU utilization, or whether they also want workflow portability from laptops and homelabs. I'm involved with GPUGo / TensorFusion, so that's the lens I'm looking through.

Didn't make sense to launch multiple 10 and 40 bucks subscriptions right at the start, because now they are competing with each other.

Also mobile version is a bit broken, but good idea and good luck!

Pretty cool idea, but whats the stack behind this? As 15-25 tok/s seems a bit low as expected SoA for most providers is around 60 tok/s and quality of life dramatically improves above that.

Can you explain the benefits over something like openrouter?

It seems crazy to me that the "Join" button does not have a price on it and yet clicking it simply forwards you to a Stripe page again with no price information on it. How am I supposed to know how much I'm about to be charged?

Is this not a more restricted version of OpenRouter? With OpenRouter you pay for credits that can be used to run any commercial or open-source model and you only pay for what you use.

So shared hosting for LLMs?

This is the most "Prompted ourselves a Shadcn UI" page I've seen in a while lol

I dig the idea! I'm curious where the costs will land with actual use.

25 t/s is barely usable. Maybe for a background runner

> Running DeepSeek V3 (685B) requires 8×H100 GPUs which is about $14k/month. Most developers only need 15-25 tok/s.

> deepseek-v3.2-685b, $40/mo/slot for ~20 tok/s, 465 slots total

> 465 users × 20 tok/s = 9,300 tok/s needed

> The node peaks at ~3,000 tok/s total. So at full capacity they can really only serve:

> 3,000 ÷ 20 = 150 concurrent users at 20 tok/s

> That's only 32% of the cohort being active simultaneously.

Can you show a comparison of cost of we went per token pricing.

This is great, thanks!

I personally would like something like this but with "regular" GPU access. Some people still use them for something other than LLMs ^^.

I read the FAQ, and I can't imagine this is going to work the way you want it to. It fundamentally doesn't make sense as a business model.

I can sign up for a cohort today, but there's not even a hint of how long it will take the cohort to fill up. The most subscribed cohort is only at 42% (and dropping), so maybe days to weeks? That's a long time to wait if you have a use case to satisfy.

And then the cohort expires, and I have to sign up for another one and play the waiting game again? Nobody wants that level of unreliability.

Also, don't say "15-25 tok/s". That is a min-max figure, but your FAQ says that this is actually a maximum. It makes no sense to measure a maximum as a range, and you state no minimum so I can only assume that it is 0 tok/s. If all users in the cohort use it simultaneously, the best they're getting is something like 1.5 tok/s (probably less), which is abyssmal.

You mention "optimization", but I have no idea what that means. It certainly doesn't mean imposing token limits, because your FAQ says that won't happen. If more than 25 users are using the cohort simultaneously, it is a physical impossibility to improve performance to the levels you advertise without sacrificing something else, like switching to a smaller model, which would essentially be fraud, or adding more GPUs which will bankrupt you at these margins. With 465 users per cohort, a large chunk of whom will be using tools like OpenClaw, nobody will ever see the performance you are offering.

The issue here is you are trying to offer affordable AI GPU nodes without operating at a loss. The entire AI industry is operating at a loss right now because of how expensive this all is. This strategy literally won't work right now unless you start courting VCs to invest tens to hundreds of millions of dollars so you can get this off the ground by operating at a loss until hopefully you turn a profit at some point in the future, but at that point developers will probably be able to run these models at home without your help.

What a brilliant idea!

Split a "it needs to run in a datacenter because its hardware requirements are so large" AI/LLM across multiple people who each want shared access to that particular model.

Sort of like the Real Estate equivalent of subletting, or splitting a larger space into smaller spaces and subletting each one...

Or, like the Web Host equivalent of splitting a single server into multiple virtual machines for shared hosting by multiple other parties, or what-have-you...

I could definitely see marketplaces similar to this, popping up in the future!

It seems like it should make AI cheaper for everyone... that is, "democratize AI"... in a "more/better/faster/cheaper" way than AI has been democratized to date...

Anyway, it's a brilliant idea!

Wishing you a lot of luck with this endeavor!

Like vast.ai and TensorDock, and presumably others.

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