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hexa00today at 7:23 PM3 repliesview on HN

Just tried it on a medium size coding/debug problem on an existing codebase, observations: - Input doesn't look faster than other models, it spends a lot of time reading Read about 5M tokens - Output is awesome, super fast as you expect from the 1500t/sec I think that's correct - Tool call is failing more than say DS4, which leads to time wasted on retries (complex tools like browser control for example) - Shell commands are still somewhat of a bottleneck

The net effect is that I spend about the same time waiting, and I still need to read that output so, at least for coding, it actually reconciles me with the 100-200t/sec you can get on DS4 or the like. Maybe that's a good sweet spot after all and faster t/sec is not where the bottleneck is.

Also maybe my setup (OMP) doesn't do the cache correctly but that's a huge cost driver... so atm it's quite pricy


Replies

peri-cltoday at 7:29 PM

> "Also maybe my setup (OMP) doesn't do the cache correctly but that's a huge cost driver... so atm it's quite pricy"

I don't believe Cerebras has a cached input pricing? They don't list one on the model page:

https://inference-docs.cerebras.ai/models/qwen-3.8-27b

edit: See the sibling discussion,

https://news.ycombinator.com/item?id=49554520#49555094 ("Input tokens, whether served from the cache or processed fresh, are billed at the standard input token rate")

show 2 replies
irthomasthomastoday at 7:58 PM

I can't believe this situation has not improved in years. Is cerebras' main business selling the hardware, then?

show 1 reply
nkhs89today at 7:45 PM

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