Comment by umairnadeem123

11 hours ago

0.2 tok/s is slow for chat but perfectly fine for batch/async workloads. I run automated content generation pipelines where a single job kicks off dozens of LLM calls (script generation, metadata, descriptions) and none of them need to be interactive. The whole job takes 20 minutes anyway because of image generation bottlenecks. Being able to run a 70B model locally for those batch calls instead of paying per-token API costs would be a significant cost reduction, even at this speed.

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umairnadeem123

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esquire_900  10 hours ago

Cost wise it does not seem very effective. .5 token / sec (the optimized one) is 3600 tokens an hour, which costs about 200-300 watts for an active 3090+system. Running 3600 tokens on open router @.4$ for llama 3.1 (3.3 costs less), is about $0,00144. That money buys you about 2-3 watts (in the Netherlands).

Great achievement for privacy inference nonetheless.

  • qoez  3 hours ago

    Open router is highly subsidized. This might be cheaper in the long run once these companies shift to taking profits

  • Aerroon  10 hours ago

    Something to consider is that input tokens have a cost too. They are typically processed much faster than output tokens. If you have long conversations then input tokens will end up being a significant part of the cost.

    It probably won't matter much here though.

  • thatwasunusual  5 hours ago

    > Cost wise it does not seem very effective.

    Why is this so damn important? Isn't it more important to end up with the best result?

    I (in Norway) use a homelab with Ollama to generate a report every morning. It's slow, but it runs between 5-6 am, energy prices are at a low, and it doesn't matter if it takes 5 or 50 minutes.