[jychang]

In practice, tps is a reflection of vram memory bandwidth during inference. So the tps tells you a lot about the hardware you're running on.

Comparing tps ratios- by saying a model is roughly 2x faster or slower than another model- can tell you a lot about the active param count.

I won't say it'll tell you everything; I have no clue what optimizations Opus may have, which can range from native FP4 experts to spec decoding with MTP to whatever. But considering chinese models like Deepseek and GLM have MTP layers (no clue if Qwen 3.5 has MTP, I haven't checked since its release), and Kimi is native int4, I'm pretty confident that there is not a 10x difference between Opus and the chinese models. I would say there's roughly a 2x-3x difference between Opus 4.5/4.6 and the chinese models at most.

[throwdbaaway]

What about the VRAM requirement for KV cache? That may matter more than memory bandwidth. With these GPUs, there are more compute capacity than memory bandwidth than VRAM.

DeepSeek got MLA, and then DSA. Qwen got gated delta-net. These inventions allow efficient inference both at home and at scale. If Anthropic got nothing here, then their inference cost can be much higher.

DeepSeek also got https://github.com/deepseek-ai/3FS that makes cached reads a lot cheaper with way longer TTL. If Anthropic didn't need to invent and uses some expensive solution like Redis, as indicated by the crappy TTL, then that also contributes to higher inference cost.

[fc417fc802]

> In practice, tps is a reflection of vram memory bandwidth during inference.

> Comparing tps ratios- by saying a model is roughly 2x faster or slower than another model- can tell you a lot about the active param count.

You sure about that? I thought you could shard between GPUs along layer boundaries during inference (but not training obviously). You just end up with an increasingly deep pipeline. So time to first token increases but aggregate tps also increases as you add additional hardware.

[jychang]

That doesn't work. Think about it a bit more.

Hint: what's in the kv cache when you start processing the 2nd token?

And that's called layer parallelism (as opposed to tensor parallelism). It allows you to run larger models (pooling vram across gpus) but does not allow you to run models faster.

Tensor parallelism DOES allow you to run models faster across multiple GPUs, but you're limited to how fast you can synchronize the all-reduce. And in general, models would have the same boost on the same hardware- so the chinese models would have the same perf multiplier as Opus.

Note that providers generally use tensor parallelism as much as they can, for all models. That usually means 8x or so.

In reality, tps ends up being a pretty good proxy for active param size when comparing different models at the same inference provider.

[fc417fc802]

Oh I see. I went and confused total aggregate throughput with per-query throughput there didn't I.