K2 Horizon lineup is out on AA, and once again AA plots are misleading.
Mirrored from r/LocalLLaMA for archival readability. Support the source by reading on the original site.
| The full K2 Horizon lineup is out on Artificial Analysis. The AA intelligence vs. parameters plots show that - 0.9B and 375B are bad - 3.7B and 7B are SOTA - 36B A4B is SOTA for hardware with poor memory bandwidth (spilled experts, Strix Halo, DGX Spark). I'm going to take the AA Intelligence Index at face value here. This post is not about it. The problem is that these models have a god-awful KV cache design. This means that you really can't use the number of parameters for "best in class" considerations, because these models heavily shift to the right on the plot if you replace parameter count on the X axis with RAM requirements. For Q4_K_M weights, no drafter, no vision, 128k kvarn4 KV cache:
Compare them to
Notes: I don't advise compressing 2~4B models to Q4 and I haven't tested these models' tolerance to weights and kv cache quantization yet. The above choices are just to keep the comparison fair. This awful context design means that
[link] [comments] |
More from r/LocalLLaMA
-
The AI race: China's open-weight models trail US closed models by just 7 months
Oct 14
-
Anthropic Executive Says Open-Source AI Models Are Higher Risk
Oct 14
-
ComfyUI v0.40.0 released
Oct 14
-
So this is how they do it --> RTX 5090, once a consumer gaming card, is now on smugglers’ menu alongside cigarettes
Oct 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.