alt Hacker NewsAgentic workloads are somewhere around 1%/0.5%/98.5% input/output/cached tokens. Cached tokens are pretty much free for inference providers (if they implement sparse and compressed attention properly) and throughput for input tokens is much higher.
Lets assume that you've got 2 million input tokens, 1 million output tokens and 98.5 million cached tokens to process. That would cost 2 * $0.14 + 1 * $0.28 + 98.5 * $0.0028 = $0.8358 with DeepSeek API pricing.
For comparison, it would take 2M / 8000 + 1M / 800 = 1500 seconds to process this amount of tokens with the linked framework, which is about $0.83 when we assume $2/hr for one MI300X.
However, other inference providers have 10 times higher prices for cached tokens, which results in a comfortable margin.
And we should not discount that DeepSeek also gets paid in data, which is probably more valuable to them.
And I believe that this framework still has some room for optimization for generation with high batch sizes.
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> should not discount that DeepSeek also gets paid in data, which is probably more valuable to them
That's agentic feedback loops for training, right? Any more detail on this, such as how they actually tell whether that data is good or not? That seems like a very hard problem, and like the value of that data is low compared to just building their own, controlled RL gyms.
Your math is a bit funny if you're assuming the 1/0.5/98.5 ratios: you doubled input and output tokens but not cached. If you double cached tokens to match your original ratio it works out to around $1.11, and if you 10x the cached token cost it's around $6.08.
Based on your $0.83 estimate, the margin isn't great. This is within shooting distance of "at cost" which is probably pretty close to what DeepSeek is operating with, ignoring the value of the data they're collecting of course.
> And I believe that this framework still has some room for optimization for generation with high batch sizes.
If that optimization can bring this scenario closer to $0.50 then it gets pretty compelling, otherwise I'm not confident.