I would rather we give up the idea of running open models on RTX cards and instead focus on running much bigger open models on H200s.

1. The hardware will eventually catch up.

2. This keeps the delta between frontier models smaller.

3. We can still fine tune and own the weights.

4. The models will be more useful, faster, and reliable.

RTX is hobbyist tier, not professional tier.

Gated cloud models from hyperscalers treat us like hobbyists in their own right.

We need equivalent scale models, but open.

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zozbot234 2 hours ago | parent | next [-]

H200s and other enterprise datacenter GPUs are completely overkill in any realistic single- or few-users inference scenario. They're hugely unbalanced towards compute capacity which will go almost entirely unused (i.e. wasted) unless you're running huge batches on a continued basis. I've argued many times that local inference engines should support batched inference on a somewhat smaller scale for a variety of reasons (especially given the unexpected effectiveness of SSD streamed inference with larger-than-RAM models), but even I don't think we can realistically go to 300x or so for real-time inference, which is the range that pencils out quite consistently from a simple roofline model of these datacenter cards.

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echelon an hour ago | parent [-]

If you're doing professional work in coding or video, you can easily saturate a single H200.

This is what RunPod-type services are for.

For instance, ComfyUI is an abomination that can't do half of what Nano Banana and Seedance 2.0 can do. And you have to sit around and wait 10x longer for single results.

I can rent an H200 for $3.50 an hour. That's INSANELY cheap.

I do not understand this split between hosted APIs and rinky-dink local RTX models. Both suck.

The ideal solution is models we own run on RunPods leveraging H200s.

I can spend $100-200/day on compute making much more value with the model outputs.

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edit: I want to respond to comments, but the damned HN rate limits keep me to five comments a day now because I'm a contrarian and say things that rile up the anti-AI folks.

You don't need to buy an H200. It's a depreciating asset. You rent one. It's cheap to rent.

	▲	spockz an hour ago \| parent \| next [-]
		Sure, to approach frontier model quality locally we need to have more power. And H200s are a way to get there. However, we need to use the tools that we have. Even if I wanted to buy a (bunch of) H200 for me and my colleagues and could get the expense approved, they are hard to source where we are. Yes. You can rent them, but I’m not sure how that affects the IP discussion. Moreover, not everyone is doing coding and video so we have different tasks that can fit quite well on relatively light laptops (Gemma et al), for relatively directed coding sessions we can make do with RTX cards, or a small step up, all the way to H200 in the workstation. Or pods thereof. We have the graphics cards and laptops with MLX right now. The H200 will take a year at least to arrive. Better get used to run stuff locally.
	▲	zozbot234 an hour ago \| parent \| prev [-]
		I'll definitely believe that for video generation models, but those are also very compute-intensive for rather middling results.

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SR2Z 2 hours ago | parent | prev | next [-]

That GPU costs 25k which means you really should have a rack to put it in. It's not realistic.

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MrLeap 2 hours ago | parent | prev [-]

There's a lot more professionals that have RTX cards than H200s. You're inevitably see more development and experimentation on things actual humans have lmao.