Thanks for the links — I went through all of them (took me a while). The point about rack density differences between SRAM-based systems like Cerebras or Groq and GPU clusters is now clear to me.
What I’m still trying to understand is the economics.
Groq seems to offer near lowest prices per million tokens and the near fastest end to end response times. That’s surprising because in my understanding, speed(latency) and the cost are trade-offs.
So I’m wondering:
Why can’t GPU-based providers can't offer cheaper but slower(high-latency) APIs?
Or do you think Groq/Cerebras are pricing much below cost (loss-leader style)?
Thank you for sharing this perspective — really insightful. I’ve been reading up on Groq’s architecture and was under the impression that their chips dedicate a significant portion of die area to on-chip SRAM (around 220MiB per chip, if I recall correctly), which struck me as quite generous compared to typical accelerators.
From die shots and materials I’ve seen, it even looks like ~40% of the die might be allocated to memory [1]. Given that, I’m curious about your point on “not enough die for memory” — is it a matter of absolute capacity still being insufficient for current model sizes, or more about the area-bandwidth tradeoff being unbalanced for inference workloads? Or perhaps something else entirely?
I’d love to understand this design tension more deeply, especially from someone with a high-level view of real-world deployments. Thanks again.
What I’m still trying to understand is the economics.
From this benchmark: https://artificialanalysis.ai/models/llama-4-scout/providers...
Groq seems to offer near lowest prices per million tokens and the near fastest end to end response times. That’s surprising because in my understanding, speed(latency) and the cost are trade-offs.
So I’m wondering: Why can’t GPU-based providers can't offer cheaper but slower(high-latency) APIs? Or do you think Groq/Cerebras are pricing much below cost (loss-leader style)?